洛阳建网站公司,游戏网游戏平台,莱芜贴吧论坛,南京logo设计公司【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#xff08;全网首发#xff09; 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#xff0c;分别是回归预测#xff0c;二分类预测和多分类预…【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
parameterscontainers.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-1.01325.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
parameterscontainers.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: 2005.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
parameterscontainers.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: 2005.特征变量敏感性分析 五、代码获取
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