【MATLAB第70期】基于MATLAB的LightGbm(LGBM)梯度增强决策树多输入单输出回归预测及多分类预测模型(全网首发)
【MATLAB第70期】基于MATLAB的LightGbm(LGBM)梯度增强决策树多输入单输出回归预测及多分类预测模型(全网首发)
一、学习资料
(LGBM)是一种基于梯度增强决策树(GBDT)算法。
本次研究三个内容,分别是回归预测,二分类预测和多分类预测
参考链接:
lightgbm原理参考链接:
训练过程评价指标metric函数参考链接:
lightgbm参数介绍参考链接:
lightgbm调参参考链接:
二、回归预测(多输入单输出)
1.数据设置
数据(103个样本,7输入1输出)
2.预测结果
3.参数设置
parameters=containers.Map; parameters('task')='train'; parameters('boosting_type')='gbdt'; parameters('metric')='rmse'; parameters('num_leaves')=31; parameters('learning_rate')=0.05; %越大,训练集效果越好 parameters('feature_fraction')=0.9; parameters('bagging_fraction')=0.8; parameters('bagging_freq')=5; parameters('num_threads')=1; parameters('verbose')=1;
4.训练过程
[ 1] train rmse 0.208872 [ 2] train rmse 0.203687 [ 3] train rmse 0.202175 [ 4] train rmse 0.200801 [ 5] train rmse 0.199554 [ 6] train rmse 0.196124 [ 7] train rmse 0.193003 [ 8] train rmse 0.192100 [ 9] train rmse 0.189259 [ 10] train rmse 0.186576 ............ [ 490] train rmse 0.052932 [ 491] train rmse 0.052870 [ 492] train rmse 0.052847 [ 493] train rmse 0.052830 [ 494] train rmse 0.052820 [ 495] train rmse 0.052771 [ 496] train rmse 0.052689 [ 497] train rmse 0.052619 [ 498] train rmse 0.052562 [ 499] train rmse 0.052506 [ 500] train rmse 0.052457 bestIteration: 500 训练集数据的R2为:0.94018 测试集数据的R2为:0.87118 训练集数据的MAE为:1.365 测试集数据的MAE为:2.3607 训练集数据的MBE为:-0.079848 测试集数据的MBE为:-1.0132
5.特征变量敏感性分析
三、分类预测(多输入单输出二分类)
1.数据设置
数据(357个样本,12输入1输出)
2.预测结果
3.参数设置
parameters=containers.Map; parameters('task')='train'; parameters('boosting_type')='gbdt'; parameters('metric')='binary_error'; parameters('num_leaves')=31; parameters('learning_rate')=0.05; parameters('feature_fraction')=0.9; parameters('bagging_fraction')=0.8; parameters('bagging_freq')=5; parameters('num_threads')=1; parameters('verbose')=0;
4.训练过程
[ 0] train binary_error 0.020833 [ 1] train binary_error 0.020833 [ 2] train binary_error 0.020833 [ 3] train binary_error 0.020833 [ 4] train binary_error 0.020833 [ 5] train binary_error 0.020833 [ 6] train binary_error 0.020833 ............ [ 191] train binary_error 0.000000 [ 192] train binary_error 0.000000 [ 193] train binary_error 0.000000 [ 194] train binary_error 0.000000 [ 195] train binary_error 0.000000 [ 196] train binary_error 0.000000 [ 197] train binary_error 0.000000 [ 198] train binary_error 0.000000 [ 199] train binary_error 0.000000 bestIteration: 200
5.特征变量敏感性分析
四、分类预测(多输入单输出多分类)
1.数据设置
数据(357个样本,12输入1输出。4分类)
2.预测结果
3.参数设置
parameters=containers.Map; parameters('task')='train'; parameters('boosting_type')='gbdt'; parameters('metric')='multi_error'; parameters('num_leaves')=31; parameters('learning_rate')=0.05; parameters('feature_fraction')=0.9; parameters('bagging_fraction')=0.8; parameters('bagging_freq')=5; parameters('num_threads')=1; parameters('verbose')=0;
4.训练过程
[ 0] train multi_error 0.112500 [ 1] train multi_error 0.066667 [ 2] train multi_error 0.066667 [ 3] train multi_error 0.066667 [ 4] train multi_error 0.062500 [ 5] train multi_error 0.058333 [ 6] train multi_error 0.054167 [ 7] train multi_error 0.054167 [ 8] train multi_error 0.058333 [ 9] train multi_error 0.058333 [ 10] train multi_error 0.054167 [ 11] train multi_error 0.054167 ............ [ 190] train multi_error 0.000000 [ 191] train multi_error 0.000000 [ 192] train multi_error 0.000000 [ 193] train multi_error 0.000000 [ 194] train multi_error 0.000000 [ 195] train multi_error 0.000000 [ 196] train multi_error 0.000000 [ 197] train multi_error 0.000000 [ 198] train multi_error 0.000000 [ 199] train multi_error 0.000000 bestIteration: 200
5.特征变量敏感性分析
五、代码获取
CSDN后台私信回复“70期”即可获取下载方式。
文章版权声明:除非注明,否则均为主机测评原创文章,转载或复制请以超链接形式并注明出处。