ML之xgboost&GBM：基于xgboost&GBM算法對(duì)HiggsBoson數(shù)據(jù)集(Kaggle競(jìng)賽)訓(xùn)練(兩模型性能PK)實(shí)現(xiàn)二分類預(yù)測(cè)

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目錄

輸出結(jié)果

設(shè)計(jì)思路

核心代碼

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輸出結(jié)果

finish loading from csv weight statistics: wpos=1522.37, wneg=904200, ratio=593.94loading data end, start to boost trees training GBM from sklearnIter Train Loss Remaining Time 1 1.2069 49.52s2 1.1437 43.51s3 1.0909 37.43s4 1.0471 30.96s5 1.0096 25.09s6 0.9775 19.90s7 0.9505 15.22s8 0.9264 9.94s9 0.9058 4.88s10 0.8878 0.00s sklearn.GBM total costs: 50.88141202926636 secondstraining xgboost [0] train-ams@0.15:3.69849 [1] train-ams@0.15:3.96339 [2] train-ams@0.15:4.26978 [3] train-ams@0.15:4.32619 [4] train-ams@0.15:4.41415 [5] train-ams@0.15:4.49395 [6] train-ams@0.15:4.64614 [7] train-ams@0.15:4.64058 [8] train-ams@0.15:4.73064 [9] train-ams@0.15:4.79447 XGBoost with 1 thread costs: 24.5108642578125 seconds [0] train-ams@0.15:3.69849 [1] train-ams@0.15:3.96339 [2] train-ams@0.15:4.26978 [3] train-ams@0.15:4.32619 [4] train-ams@0.15:4.41415 [5] train-ams@0.15:4.49395 [6] train-ams@0.15:4.64614 [7] train-ams@0.15:4.64058 [8] train-ams@0.15:4.73064 [9] train-ams@0.15:4.79447 XGBoost with 2 thread costs: 11.449955940246582 seconds [0] train-ams@0.15:3.69849 [1] train-ams@0.15:3.96339 [2] train-ams@0.15:4.26978 [3] train-ams@0.15:4.32619 [4] train-ams@0.15:4.41415 [5] train-ams@0.15:4.49395 [6] train-ams@0.15:4.64614 [7] train-ams@0.15:4.64058 [8] train-ams@0.15:4.73064 [9] train-ams@0.15:4.79447 XGBoost with 4 thread costs: 8.809934616088867 seconds [0] train-ams@0.15:3.69849 [1] train-ams@0.15:3.96339 [2] train-ams@0.15:4.26978 [3] train-ams@0.15:4.32619 [4] train-ams@0.15:4.41415 [5] train-ams@0.15:4.49395 [6] train-ams@0.15:4.64614 [7] train-ams@0.15:4.64058 [8] train-ams@0.15:4.73064 [9] train-ams@0.15:4.79447 XGBoost with 8 thread costs: 7.875434875488281 seconds XGBoost total costs: 52.64618968963623 seconds

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設(shè)計(jì)思路

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核心代碼

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總結(jié)

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