In [ ]:
import numpy as np
import matplotlib.pyplot as plt
import scipy
from numpy.random import randn # Gaussian random number
In [ ]:
def sq_loss(y):
if len(y) == 0:
return (0)
else:
y_bar = np.mean(y)
return (np.linalg.norm(y-y_bar)**2)
In [ ]:
def branch(x, y, S, rf=0):
if rf == 0:
m = x.shape[1]
if x.shape[0] == 0:
return ([0, 0, 0, 0, 0, 0, 0])
best_score = np.inf
for j in range(x.shape[1]):
for i in S:
left = []
right = []
for k in S:
if x[k, j] < x[i, j]:
left.append(k)
else:
right.append(k)
left_score = f(y[left])
right_score = f(y[right])
score = left_score + right_score
if score < best_score:
best_score = score
i_1 = i
j_1 = j
left_1 = left
right_1 = right
left_score_1 = left_score
right_score_1 = right_score
return [i_1, j_1, left_1, right_1, best_score, left_score_1, right_score_1]
In [ ]:
f = sq_loss
n = 100
p = 5
x = randn(n, p)
y = randn(n)
S = np.random.choice(n, 10, replace=False)
branch(x, y, S)
Out[ ]:
[14, 2, [32, 85, 46, 23, 3, 39, 59, 97, 70], [14], 6.104310789589534, 6.104310789589534, 0.0]
71¶
In [ ]:
class Stack:
def __init__(self, parent, set, score):
self.parent = parent
self.set = set
self.score = score
In [ ]:
class Node:
def __init__(self, parent, j, th, set):
self.parent = parent
self.j = j
self.th = th
self.set = set
In [ ]:
def dt(x, y, alpha=0, n_min=1, rf=0):
if rf == 0:
m = x.shape[1]
# Construct a stack consisting of one element. Initialize the decision tree.
stack = [Stack(0, list(range(x.shape[0])), f(y))] # Function f is global.
node = []
k = -1
# Retrieve the last element of the stack to update the decision tree.
while len(stack) > 0:
popped = stack.pop()
k = k+1
i, j, left, right, score, left_score, right_score = branch(x, y, popped.set, rf)
if popped.score-score < alpha or len(popped.set) < n_min or len(left) == 0 or len(right) == 0:
node.append(Node(popped.parent, -1, 0, popped.set))
else:
node.append(Node(popped.parent, j, x[i, j], popped.set))
stack.append(Stack(k, right, right_score))
stack.append(Stack(k, left, left_score))
# Set the values of node.left and node.right below this point.
for h in range(k, -1, -1):
node[h].left = 0
node[h].right = 0
for h in range(k, 0, -1):
pa = node[h].parent
if node[pa].right == 0:
node[pa].right = h
else:
node[pa].left = h
# Calculate the value of node.center below this point.
if f == sq_loss:
g = np.mean
else:
g = mode_max
for h in range(k+1):
if node[h].j == -1:
node[h].center = g(y[node[h].set])
else:
node[h].center = 0
return (node)
In [ ]:
df = np.loadtxt("boston.txt", delimiter="\t")
X = np.array(df[:, [0, 2, 4, 5, 6, 7, 9, 10, 11, 12]])
p = X.shape[1]
y = np.array(df[:, 13])
f = sq_loss
node = dt(X, y, n_min=50)
len(node)
Out[ ]:
37
In [ ]:
!pip uninstall igraph -y
!pip uninstall python-igraph -y
!pip install python-igraph
!pip install cairocffi
!pip install pycairo
Found existing installation: igraph 0.11.5 Uninstalling igraph-0.11.5: Successfully uninstalled igraph-0.11.5 Found existing installation: python-igraph 0.11.5 Uninstalling python-igraph-0.11.5: Successfully uninstalled python-igraph-0.11.5 Collecting python-igraph Using cached python_igraph-0.11.5-py3-none-any.whl (9.1 kB) Collecting igraph==0.11.5 (from python-igraph) Using cached igraph-0.11.5-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB) Requirement already satisfied: texttable>=1.6.2 in /usr/local/lib/python3.10/dist-packages (from igraph==0.11.5->python-igraph) (1.7.0) Installing collected packages: igraph, python-igraph Successfully installed igraph-0.11.5 python-igraph-0.11.5
Requirement already satisfied: cairocffi in /usr/local/lib/python3.10/dist-packages (1.7.0) Requirement already satisfied: cffi>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from cairocffi) (1.16.0) Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.1.0->cairocffi) (2.22) Collecting pycairo Using cached pycairo-1.26.0.tar.gz (346 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... done Building wheels for collected packages: pycairo error: subprocess-exited-with-error × Building wheel for pycairo (pyproject.toml) did not run successfully. │ exit code: 1 ╰─> See above for output. note: This error originates from a subprocess, and is likely not a problem with pip. Building wheel for pycairo (pyproject.toml) ... error ERROR: Failed building wheel for pycairo Failed to build pycairo ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects
In [ ]:
from igraph import *
In [ ]:
def draw_graph(node):
r = len(node)
col = []
for h in range(r):
col.append(node[h].j)
colorlist = ["#ffffff", "#fff8ff", "#fcf9ce", "#d6fada", "#d7ffff",
"#d9f2f8", "#fac8be", "#ffebff", "#ffffe0", "#fdf5e6",
"#fac8be", "#f8ecd5", "#ee82ee"]
color = [colorlist[col[i]] for i in range(r)]
edge = []
for h in range(1, r):
edge.append([node[h].parent, h])
g = Graph(edges=edge, directed=True)
layout = g.layout_reingold_tilford(root=[0])
out = plot(g, vertex_size=15, layout=layout, bbox=(300, 300),
vertex_label=list(range(r)), vertex_color=color)
return (out)
In [ ]:
draw_graph(node)
Out[ ]:
72¶
In [ ]:
def value(u, node):
r = 0
while node[r].j != -1:
if u[node[r].j] < node[r].th:
r = node[r].left
else:
r = node[r].right
return (node[r].center)
In [ ]:
n = 100
df = np.loadtxt("boston.txt", delimiter="\t")
X = np.array(df[0:n, [0, 2, 4, 5, 6, 7, 9, 10, 11, 12]])
p = X.shape[1]
y = np.array(df[0:n, 13])
f = sq_loss
alpha_seq = np.arange(0, 1.5, 0.1)
s = int(n/10)
out = []
for alpha in alpha_seq:
SS = 0
for h in range(10):
test = list(range(h*s, h*s+s))
train = list(set(range(n)) - set(test))
node = dt(X[train, :], y[train], alpha=alpha)
for t in test:
SS = SS + (y[t] - value(X[t, :], node))**2
print(SS / n)
out.append(SS / n)
plt.plot(alpha_seq, out)
plt.xlabel("alpha")
plt.ylabel("Squared Error")
plt.title("Optimal alpha via CV (N=100)")
11.116099999999996 11.080052777777773 11.033638888888886 10.984323611111108 10.899468055555552 10.829624305555553 10.834198228458046 10.806015589569158 10.881876006235824 10.962739873456787 10.92003709567901 10.702102453955654 10.699702453955654 10.886902453955656 10.883480231733433
Out[ ]:
Text(0.5, 1.0, 'Optimal alpha via CV (N=100)')
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In [ ]:
df = np.loadtxt("boston.txt", delimiter="\t")
X = np.array(df[0:n, [0, 2, 4, 5, 6, 7, 9, 10, 11, 12]])
p = X.shape[1]
y = np.array(df[0:n, 13])
n_min_seq = np.arange(1, 13, 1)
s = int(n / 10)
out = []
for n_min in n_min_seq:
SS = 0
for h in range(10):
test = list(range(h*s, h*s+s))
train = list(set(range(n)) - set(test))
node = dt(X[train, :], y[train], n_min=n_min)
for t in test:
SS = SS + (y[t] - value(X[t, :], node))**2
print(SS / n)
out.append(SS / n)
plt.plot(n_min_seq, out)
plt.xlabel("n_min")
plt.ylabel("Squared Error")
plt.title("Optimal n_min via CV (N=100)")
11.116099999999996 11.116099999999996 11.052175 10.699644444444445 10.971056944444445 10.772515444444448 10.774706666666672 10.682569513038551 10.582074200538548 11.954330518250815 12.337553746267947 12.548108892273458
Out[ ]:
Text(0.5, 1.0, 'Optimal n_min via CV (N=100)')
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73¶
In [ ]:
def branch(x, y, S, rf=0): #
if rf == 0: #
T = np.arange(x.shape[1]) #
else: #
T = np.random.choice(x.shape[1], rf, replace=False) #
if x.shape[0] == 0:
return ([0, 0, 0, 0, 0, 0, 0])
best_score = np.inf
for j in T: #
for i in S:
left = []
right = []
for k in S:
if x[k, j] < x[i, j]:
left.append(k)
else:
right.append(k)
left_score = f(y[left])
right_score = f(y[right])
score = left_score + right_score
if score < best_score:
best_score = score
i_1 = i
j_1 = j
left_1 = left
right_1 = right
left_score_1 = left_score
right_score_1 = right_score
return [i_1, j_1, left_1, right_1, best_score, left_score_1, right_score_1]
In [ ]:
def rf(z):
z = np.array(z, dtype=np.int64)
zz = []
for b in range(B):
u = sum([mode_max(z[range(b+1), i]) == y[i+100] for i in range(50)])
zz.append(u)
return (zz)
In [ ]:
def freq(y):
y = list(y)
return ([y.count(i) for i in set(y)])
In [ ]:
def mode(y):
n = len(y)
if n == 0:
return (0)
return (max(freq(y)))
In [ ]:
def mis_match(y):
return (len(y)-mode(y))
In [ ]:
def mode_max(y):
if len(y) == 0:
return (-np.inf)
count = np.bincount(y)
return (np.argmax(count))
In [ ]:
from sklearn.datasets import load_iris
In [ ]:
iris = load_iris()
iris.target_names
f = mis_match
n = iris.data.shape[0]
order = np.random.choice(n, n, replace=False) # Sort
X = iris.data[order, :]
y = iris.target[order]
train = list(range(100))
test = list(range(100, 150))
B = 100
plt.ylim([35, 55])
m_seq = [1, 2, 3, 4]
c_seq = ["r", "b", "g", "y"]
label_seq = ["m=1", "m=2", "m=3", "m=4"]
plt.xlabel("Iterations b")
plt.ylabel("Number of Correct Predictions on 50 Test Data")
plt.title("Random Forest")
for m in m_seq:
z = np.zeros((B, 50))
for b in range(B):
index = np.random.choice(train, 100, replace=True)
node = dt(X[index, :], y[index], n_min=2, rf=m)
for i in test:
z[b, i-100] = value(X[i, ], node)
plt.plot(list(range(B)), np.array(rf(z))-0.2*(m-2),
label=label_seq[m-1], linewidth=0.8, c=c_seq[m-1])
plt.legend(loc="lower right")
plt.axhline(y=50, c="b", linewidth=0.5, linestyle="dashed")
Out[ ]:
<matplotlib.lines.Line2D at 0x79498130e590>
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74¶
In [ ]:
import lightgbm as lgb
In [ ]:
df = np.loadtxt("boston.txt", delimiter="\t")
X = np.array(df[:, 0:12])
p = X.shape[1]
y = np.array(df[:, 13])
train = list(range(200))
test = list(range(200, 300))
B = 200
lgb_train = lgb.Dataset(X[train, :], y[train])
lgb_eval = lgb.Dataset(X[test, :], y[test], reference=lgb_train)
B = 5000
nn_seq = list(range(1, 10, 1)) + list(range(10, 91, 10)) + list(range(100, B, 50))
out_set = []
for d in range(1, 4):
lgbm_params = {
"objective": "regression",
"metric": "rmse",
"num_leaves": d+1,
"learning_rate": 0.001
}
out = []
for nn in nn_seq:
model = lgb.train(lgbm_params, lgb_train,
valid_sets=lgb_eval, num_boost_round=nn)
z = model.predict(X[test, :], num_iteration=model.best_iteration)
out.append(sum((z-y[test])**2) / 100)
out_set.append(out)
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000132 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000110 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000055 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000085 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000087 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000059 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000057 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000057 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000058 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000053 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000057 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000030 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000090 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000092 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000039 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000076 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000076 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000075 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000082 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000072 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000032 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000091 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.114980 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000144 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.080022 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000072 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000076 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000148 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000086 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000030 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000088 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000077 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000083 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000079 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000085 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000076 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000074 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000584 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000076 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000072 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000079 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000078 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000078 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000073 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000076 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000072 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000040 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083212 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.101582 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000127 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000078 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000030 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000084 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.034996 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000075 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000058 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000058 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000053 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000051 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000053 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000055 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000056 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000059 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000059 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000058 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000058 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000089 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000089 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000032 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000078 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000068 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000078 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000067 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.022544 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000066 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000078 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000069 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000104 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000030 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000093 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000084 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000060 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000065 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000071 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000064 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000062 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000063 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000083 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 430 [LightGBM] [Info] Number of data points in the train set: 200, number of used features: 11 [LightGBM] [Info] Start training from score 23.261000
In [ ]:
# Display on the graph
plt.ylim([0, 80])
c_seq = ["r", "b", "g"]
label_seq = ["d=1", "d=2", "d=3"]
plt.xlabel("Number of Trees Generated")
plt.ylabel("Mean Squared Error on Test Data")
plt.title("lightgbm Package (lambda=0.001)")
for d in range(1, 4):
plt.plot(nn_seq, out_set[d-1], label=label_seq[d-1],linewidth=0.8, c=c_seq[d-1])
plt.legend(loc="upper right")
Out[ ]:
<matplotlib.legend.Legend at 0x794976573190>
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