【MATLAB第70期】基于MATLAB的LightGbm(LGBM)梯度增強決策樹多輸入單輸出回歸預(yù)測及多分類預(yù)測模型（全網(wǎng)首發(fā)）
一、學習資料
(LGBM)是一種基于梯度增強決策樹(GBDT)算法。
本次研究三個內(nèi)容，分別是回歸預(yù)測，二分類預(yù)測和多分類預(yù)測
參考鏈接：
lightgbm原理參考鏈接：
訓練過程評價指標metric函數(shù)參考鏈接：
lightgbm參數(shù)介紹參考鏈接：
lightgbm調(diào)參參考鏈接：
二、回歸預(yù)測（多輸入單輸出）
1.數(shù)據(jù)設(shè)置
數(shù)據(jù)（103個樣本，7輸入1輸出）
2.預(yù)測結(jié)果
3.參數(shù)設(shè)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，訓練集效果越好
parameters('feature_fraction')=0.9;
parameters('bagging_fraction')=0.8;
parameters('bagging_freq')=5;
parameters('num_threads')=1;
parameters('verbose')=1;
4.訓練過程
[ 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
訓練集數(shù)據(jù)的R2為：0.94018
測試集數(shù)據(jù)的R2為：0.87118
訓練集數(shù)據(jù)的MAE為：1.365
測試集數(shù)據(jù)的MAE為：2.3607
訓練集數(shù)據(jù)的MBE為：-0.079848
測試集數(shù)據(jù)的MBE為：-1.0132
5.特征變量敏感性分析
三、分類預(yù)測（多輸入單輸出二分類）
1.數(shù)據(jù)設(shè)置
數(shù)據(jù)（357個樣本，12輸入1輸出）
2.預(yù)測結(jié)果
3.參數(shù)設(shè)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.訓練過程
[ 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.特征變量敏感性分析
四、分類預(yù)測（多輸入單輸出多分類）
1.數(shù)據(jù)設(shè)置
數(shù)據(jù)（357個樣本，12輸入1輸出。4分類）
2.預(yù)測結(jié)果
3.參數(shù)設(shè)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.訓練過程
[ 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.特征變量敏感性分析
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