python|LightGBM模型

最新推荐文章于 2025-10-17 15:08:16 发布

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最新推荐文章于 2025-10-17 15:08:16 发布 · 4.2k 阅读

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#机器学习 #lgb

本文详细介绍了如何在Python中使用LightGBM进行机器学习建模，包括数据预处理、模型训练、参数调优及模型评估等关键步骤。LightGBM作为一款高效的梯度提升框架，以其速度快、内存占用低和准确度高等特点深受数据科学家喜爱。

# -*- coding: utf-8 -*-
"""
Created on Fri Jun 12 16:20:17 2020

@author: weiping
"""

import xgboost as xgb
import lightgbm as lgb  
from sklearn.model_selection  import train_test_split
from sklearn.metrics import *
from sklearn.datasets import load_iris

iris = load_iris()

data_x = iris.data 
data_y = iris.target

x_tr,x_te,y_tr,y_te = train_test_split(data_x,data_y,train_size = 0.7,random_state =22)
#XGBboost模型
xgb_model = xgb.XGBClassifier()
xgb_model.fit(x_tr,y_tr)
xgb_predict = xgb_model.predict(x_te)

print("xgb准确率：" ,str(xgb_model.score(x_te,y_te)))
#print("roc_auc_score:",str(roc_auc_score(y_te,xgb_predict))) 不支持多分类
print("precision_score:",str(precision_score(y_te,xgb_predict,average = 'weighted')))
print("recall_score:" , str(recall_score(y_te,xgb_predict,average = 'weighted')))
print("f1_score:",str(f1_score(y_te,xgb_predict,average = 'weighted')))
'''
xgb准确率： 0.9333333333333333
precision_score: 0.9344662309368191
recall_score: 0.9333333