LightGBM算法详解(教你一文掌握LightGBM所有知识点)

2024年2月1日发(作者：)

#数据预处理import numpy as np

import as plt

import pandas as pd

# Importing the dataset

dataset = _csv('...inputSocial_Network_')

X = [:, [2, 3]].values

y = [:, 4].values

# Splitting the dataset into the Training set and Test set

from _validation import train_test_split

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

# Feature Scaling

from cessing import StandardScaler

sc = StandardScaler()

x_train = _transform(x_train)

x_test = orm(x_test)#模型构建和训练import lightgbm as lgb

d_train = t(x_train, label=y_train)

params = {}

params['learning_rate'] = 0.003

params['boosting_type'] = 'gbdt'

params['objective'] = 'binary'

params['metric'] = 'binary_logloss'

params['sub_feature'] = 0.5

params['num_leaves'] = 10

params['min_data'] = 50

params['max_depth'] = 10

clf = (params, d_train, 100)#模型预测#Prediction

y_pred=t(x_test)

#convert into binary values

for i in range(0,99):

if y_pred[i]>=.5: # setting threshold to .5

y_pred[i]=1

else:

y_pred[i]=0#准确率计算#Confusion matrix

from s import confusion_matrix

cm = confusion_matrix(y_test, y_pred)

#Accuracy

from s import accuracy_score

accuracy=accuracy_score(y_pred,y_test)结果：调参: