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ML之XGBoost：利用XGBoost算法对波士顿数据集回归预测(模型调参【2种方法，ShuffleSplit+GridSearchCV、TimeSeriesSplitGSCV】、模型评估)

2020-09-04 21:45 1531 查看

利用XGBoost算法对波士顿数据集回归预测

T1、ShuffleSplit+GSCV模型调参

输出XGBR_GSCV模型最佳得分、最优参数：0.8630,{'learning_rate': 0.12, 'max_depth': 3, 'n_estimators': 200}
XGBR_Shuffle_GSCV time： 256.7015066994206
XGBoost Score value: 0.8536645272887292
XGBoost R2 value: 0.8536645272887292
XGBoost MAE value: 2.1987844654894246
XGBoost RMSE value: 3.368537070469827

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0.807687 (0.050865) with: {'learning_rate': 0.21, 'max_depth': 9, 'n_estimators': 50}
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0.807783 (0.050856) with: {'learning_rate': 0.21, 'max_depth': 9, 'n_estimators': 150}
0.807783 (0.050856) with: {'learning_rate': 0.21, 'max_depth': 9, 'n_estimators': 200}
0.807752 (0.054800) with: {'learning_rate': 0.21, 'max_depth': 11, 'n_estimators': 50}
0.807782 (0.054819) with: {'learning_rate': 0.21, 'max_depth': 11, 'n_estimators': 100}
0.807782 (0.054819) with: {'learning_rate': 0.21, 'max_depth': 11, 'n_estimators': 150}
0.807782 (0.054819) with: {'learning_rate': 0.21, 'max_depth': 11, 'n_estimators': 200}
0.806377 (0.052411) with: {'learning_rate': 0.21, 'max_depth': 13, 'n_estimators': 50}
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0.806415 (0.052447) with: {'learning_rate': 0.21, 'max_depth': 13, 'n_estimators': 150}
0.806415 (0.052447) with: {'learning_rate': 0.21, 'max_depth': 13, 'n_estimators': 200}
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0.807671 (0.052247) with: {'learning_rate': 0.21, 'max_depth': 15, 'n_estimators': 150}
0.807671 (0.052247) with: {'learning_rate': 0.21, 'max_depth': 15, 'n_estimators': 200}
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0.831840 (0.050128) with: {'learning_rate': 0.24, 'max_depth': 1, 'n_estimators': 200}
0.857136 (0.046683) with: {'learning_rate': 0.24, 'max_depth': 3, 'n_estimators': 50}
0.859483 (0.045770) with: {'learning_rate': 0.24, 'max_depth': 3, 'n_estimators': 100}
0.859945 (0.045322) with: {'learning_rate': 0.24, 'max_depth': 3, 'n_estimators': 150}
0.859801 (0.045460) with: {'learning_rate': 0.24, 'max_depth': 3, 'n_estimators': 200}
0.850955 (0.049845) with: {'learning_rate': 0.24, 'max_depth': 5, 'n_estimators': 50}
0.850508 (0.050615) with: {'learning_rate': 0.24, 'max_depth': 5, 'n_estimators': 100}
0.850476 (0.050598) with: {'learning_rate': 0.24, 'max_depth': 5, 'n_estimators': 150}
0.850498 (0.050571) with: {'learning_rate': 0.24, 'max_depth': 5, 'n_estimators': 200}
0.811733 (0.057462) with: {'learning_rate': 0.24, 'max_depth': 7, 'n_estimators': 50}
0.811878 (0.057440) with: {'learning_rate': 0.24, 'max_depth': 7, 'n_estimators': 100}
0.811878 (0.057440) with: {'learning_rate': 0.24, 'max_depth': 7, 'n_estimators': 150}
0.811878 (0.057440) with: {'learning_rate': 0.24, 'max_depth': 7, 'n_estimators': 200}
0.807850 (0.060428) with: {'learning_rate': 0.24, 'max_depth': 9, 'n_estimators': 50}
0.807894 (0.060424) with: {'learning_rate': 0.24, 'max_depth': 9, 'n_estimators': 100}
0.807894 (0.060424) with: {'learning_rate': 0.24, 'max_depth': 9, 'n_estimators': 150}
0.807894 (0.060424) with: {'learning_rate': 0.24, 'max_depth': 9, 'n_estimators': 200}
0.802244 (0.059772) with: {'learning_rate': 0.24, 'max_depth': 11, 'n_estimators': 50}
0.802246 (0.059770) with: {'learning_rate': 0.24, 'max_depth': 11, 'n_estimators': 100}
0.802246 (0.059770) with: {'learning_rate': 0.24, 'max_depth': 11, 'n_estimators': 150}
0.802246 (0.059770) with: {'learning_rate': 0.24, 'max_depth': 11, 'n_estimators': 200}
0.807033 (0.061863) with: {'learning_rate': 0.24, 'max_depth': 13, 'n_estimators': 50}
0.807040 (0.061864) with: {'learning_rate': 0.24, 'max_depth': 13, 'n_estimators': 100}
0.807040 (0.061864) with: {'learning_rate': 0.24, 'max_depth': 13, 'n_estimators': 150}
0.807040 (0.061864) with: {'learning_rate': 0.24, 'max_depth': 13, 'n_estimators': 200}
0.804463 (0.063512) with: {'learning_rate': 0.24, 'max_depth': 15, 'n_estimators': 50}
0.804475 (0.063511) with: {'learning_rate': 0.24, 'max_depth': 15, 'n_estimators': 100}
0.804475 (0.063511) with: {'learning_rate': 0.24, 'max_depth': 15, 'n_estimators': 150}
0.804475 (0.063511) with: {'learning_rate': 0.24, 'max_depth': 15, 'n_estimators': 200}
0.821150 (0.049890) with: {'learning_rate': 0.27, 'max_depth': 1, 'n_estimators': 50}
0.830742 (0.049213) with: {'learning_rate': 0.27, 'max_depth': 1, 'n_estimators': 100}
0.831787 (0.050679) with: {'learning_rate': 0.27, 'max_depth': 1, 'n_estimators': 150}
0.830709 (0.051395) with: {'learning_rate': 0.27, 'max_depth': 1, 'n_estimators': 200}
0.857873 (0.052985) with: {'learning_rate': 0.27, 'max_depth': 3, 'n_estimators': 50}
0.861046 (0.050332) with: {'learning_rate': 0.27, 'max_depth': 3, 'n_estimators': 100}
0.861720 (0.049712) with: {'learning_rate': 0.27, 'max_depth': 3, 'n_estimators': 150}
0.861372 (0.049998) with: {'learning_rate': 0.27, 'max_depth': 3, 'n_estimators': 200}
0.847206 (0.051159) with: {'learning_rate': 0.27, 'max_depth': 5, 'n_estimators': 50}
0.847094 (0.051525) with: {'learning_rate': 0.27, 'max_depth': 5, 'n_estimators': 100}
0.847037 (0.051513) with: {'learning_rate': 0.27, 'max_depth': 5, 'n_estimators': 150}
0.847035 (0.051514) with: {'learning_rate': 0.27, 'max_depth': 5, 'n_estimators': 200}
0.803202 (0.050403) with: {'learning_rate': 0.27, 'max_depth': 7, 'n_estimators': 50}
0.803226 (0.050360) with: {'learning_rate': 0.27, 'max_depth': 7, 'n_estimators': 100}
0.803226 (0.050360) with: {'learning_rate': 0.27, 'max_depth': 7, 'n_estimators': 150}
0.803226 (0.050360) with: {'learning_rate': 0.27, 'max_depth': 7, 'n_estimators': 200}
0.806931 (0.056619) with: {'learning_rate': 0.27, 'max_depth': 9, 'n_estimators': 50}
0.806930 (0.056616) with: {'learning_rate': 0.27, 'max_depth': 9, 'n_estimators': 100}
0.806930 (0.056616) with: {'learning_rate': 0.27, 'max_depth': 9, 'n_estimators': 150}
0.806930 (0.056616) with: {'learning_rate': 0.27, 'max_depth': 9, 'n_estimators': 200}
0.802898 (0.060888) with: {'learning_rate': 0.27, 'max_depth': 11, 'n_estimators': 50}
0.802900 (0.060887) with: {'learning_rate': 0.27, 'max_depth': 11, 'n_estimators': 100}
0.802900 (0.060887) with: {'learning_rate': 0.27, 'max_depth': 11, 'n_estimators': 150}
0.802900 (0.060887) with: {'learning_rate': 0.27, 'max_depth': 11, 'n_estimators': 200}
0.799887 (0.055534) with: {'learning_rate': 0.27, 'max_depth': 13, 'n_estimators': 50}
0.799886 (0.055534) with: {'learning_rate': 0.27, 'max_depth': 13, 'n_estimators': 100}
0.799886 (0.055534) with: {'learning_rate': 0.27, 'max_depth': 13, 'n_estimators': 150}
0.799886 (0.055534) with: {'learning_rate': 0.27, 'max_depth': 13, 'n_estimators': 200}
0.800050 (0.056070) with: {'learning_rate': 0.27, 'max_depth': 15, 'n_estimators': 50}
0.800051 (0.056071) with: {'learning_rate': 0.27, 'max_depth': 15, 'n_estimators': 100}
0.800051 (0.056071) with: {'learning_rate': 0.27, 'max_depth': 15, 'n_estimators': 150}
0.800051 (0.056071) with: {'learning_rate': 0.27, 'max_depth': 15, 'n_estimators': 200}
0.819760 (0.053159) with: {'learning_rate': 0.3, 'max_depth': 1, 'n_estimators': 50}
0.827881 (0.052422) with: {'learning_rate': 0.3, 'max_depth': 1, 'n_estimators': 100}
0.828890 (0.053188) with: {'learning_rate': 0.3, 'max_depth': 1, 'n_estimators': 150}
0.829313 (0.052821) with: {'learning_rate': 0.3, 'max_depth': 1, 'n_estimators': 200}
0.854570 (0.052578) with: {'learning_rate': 0.3, 'max_depth': 3, 'n_estimators': 50}
0.857880 (0.050891) with: {'learning_rate': 0.3, 'max_depth': 3, 'n_estimators': 100}
0.858274 (0.051601) with: {'learning_rate': 0.3, 'max_depth': 3, 'n_estimators': 150}
0.858004 (0.051720) with: {'learning_rate': 0.3, 'max_depth': 3, 'n_estimators': 200}
0.837382 (0.048349) with: {'learning_rate': 0.3, 'max_depth': 5, 'n_estimators': 50}
0.837511 (0.048140) with: {'learning_rate': 0.3, 'max_depth': 5, 'n_estimators': 100}
0.837538 (0.048128) with: {'learning_rate': 0.3, 'max_depth': 5, 'n_estimators': 150}
0.837538 (0.048128) with: {'learning_rate': 0.3, 'max_depth': 5, 'n_estimators': 200}
0.814336 (0.051179) with: {'learning_rate': 0.3, 'max_depth': 7, 'n_estimators': 50}
0.814383 (0.051205) with: {'learning_rate': 0.3, 'max_depth': 7, 'n_estimators': 100}
0.814383 (0.051205) with: {'learning_rate': 0.3, 'max_depth': 7, 'n_estimators': 150}
0.814383 (0.051205) with: {'learning_rate': 0.3, 'max_depth': 7, 'n_estimators': 200}
0.806069 (0.051545) with: {'learning_rate': 0.3, 'max_depth': 9, 'n_estimators': 50}
0.806069 (0.051545) with: {'learning_rate': 0.3, 'max_depth': 9, 'n_estimators': 100}
0.806069 (0.051545) with: {'learning_rate': 0.3, 'max_depth': 9, 'n_estimators': 150}
0.806069 (0.051545) with: {'learning_rate': 0.3, 'max_depth': 9, 'n_estimators': 200}
0.810698 (0.049687) with: {'learning_rate': 0.3, 'max_depth': 11, 'n_estimators': 50}
0.810698 (0.049687) with: {'learning_rate': 0.3, 'max_depth': 11, 'n_estimators': 100}
0.810698 (0.049687) with: {'learning_rate': 0.3, 'max_depth': 11, 'n_estimators': 150}
0.810698 (0.049687) with: {'learning_rate': 0.3, 'max_depth': 11, 'n_estimators': 200}
0.807420 (0.051992) with: {'learning_rate': 0.3, 'max_depth': 13, 'n_estimators': 50}
0.807420 (0.051992) with: {'learning_rate': 0.3, 'max_depth': 13, 'n_estimators': 100}
0.807420 (0.051992) with: {'learning_rate': 0.3, 'max_depth': 13, 'n_estimators': 150}
0.807420 (0.051992) with: {'learning_rate': 0.3, 'max_depth': 13, 'n_estimators': 200}
0.803384 (0.054540) with: {'learning_rate': 0.3, 'max_depth': 15, 'n_estimators': 50}
0.803384 (0.054540) with: {'learning_rate': 0.3, 'max_depth': 15, 'n_estimators': 100}
0.803384 (0.054540) with: {'learning_rate': 0.3, 'max_depth': 15, 'n_estimators': 150}
0.803384 (0.054540) with: {'learning_rate': 0.3, 'max_depth': 15, 'n_estimators': 200}

T2、TimeSeriesSplit=GSCV模型调参

输出XGBR_GSCV模型最佳得分、最优参数：0.8772,{'learning_rate': 0.15, 'max_depth': 3, 'n_estimators': 200}
XGBR_TimeS_GSCV time： 365.73213645175
XGBoost Score value: 0.8392863414585984
XGBoost R2 value: 0.8392863414585984
XGBoost MAE value: 2.265871170374352
XGBoost RMSE value: 3.5301480357113575

[code]Fitting 6 folds for each of 320 candidates, totalling 1920 fits
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 1920 out of 1920 | elapsed:  6.1min finished
0.601753 (0.041626) with: {'learning_rate': 0.03, 'max_depth': 1, 'n_estimators': 50}
0.741963 (0.052567) with: {'learning_rate': 0.03, 'max_depth': 1, 'n_estimators': 100}
0.769275 (0.057973) with: {'learning_rate': 0.03, 'max_depth': 1, 'n_estimators': 150}
0.777850 (0.062691) with: {'learning_rate': 0.03, 'max_depth': 1, 'n_estimators': 200}
0.761917 (0.044601) with: {'learning_rate': 0.03, 'max_depth': 3, 'n_estimators': 50}
0.849871 (0.032589) with: {'learning_rate': 0.03, 'max_depth': 3, 'n_estimators': 100}
0.860943 (0.036123) with: {'learning_rate': 0.03, 'max_depth': 3, 'n_estimators': 150}
0.865884 (0.036296) with: {'learning_rate': 0.03, 'max_depth': 3, 'n_estimators': 200}
0.768191 (0.046584) with: {'learning_rate': 0.03, 'max_depth': 5, 'n_estimators': 50}
0.847475 (0.037179) with: {'learning_rate': 0.03, 'max_depth': 5, 'n_estimators': 100}
0.857618 (0.034498) with: {'learning_rate': 0.03, 'max_depth': 5, 'n_estimators': 150}
0.860371 (0.034667) with: {'learning_rate': 0.03, 'max_depth': 5, 'n_estimators': 200}
0.762532 (0.043118) with: {'learning_rate': 0.03, 'max_depth': 7, 'n_estimators': 50}
0.838141 (0.032139) with: {'learning_rate': 0.03, 'max_depth': 7, 'n_estimators': 100}
0.846885 (0.027194) with: {'learning_rate': 0.03, 'max_depth': 7, 'n_estimators': 150}
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0.754782 (0.053572) with: {'learning_rate': 0.03, 'max_depth': 11, 'n_estimators': 50}
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0.838982 (0.034142) with: {'learning_rate': 0.03, 'max_depth': 11, 'n_estimators': 150}
0.841296 (0.031967) with: {'learning_rate': 0.03, 'max_depth': 11, 'n_estimators': 200}
0.756791 (0.051747) with: {'learning_rate': 0.03, 'max_depth': 13, 'n_estimators': 50}
0.830920 (0.039090) with: {'learning_rate': 0.03, 'max_depth': 13, 'n_estimators': 100}
0.840551 (0.032427) with: {'learning_rate': 0.03, 'max_depth': 13, 'n_estimators': 150}
0.843931 (0.030071) with: {'learning_rate': 0.03, 'max_depth': 13, 'n_estimators': 200}
0.756117 (0.054046) with: {'learning_rate': 0.03, 'max_depth': 15, 'n_estimators': 50}
0.831666 (0.040286) with: {'learning_rate': 0.03, 'max_depth': 15, 'n_estimators': 100}
0.840035 (0.034584) with: {'learning_rate': 0.03, 'max_depth': 15, 'n_estimators': 150}
0.843151 (0.032286) with: {'learning_rate': 0.03, 'max_depth': 15, 'n_estimators': 200}
0.745626 (0.052724) with: {'learning_rate': 0.06, 'max_depth': 1, 'n_estimators': 50}
0.777825 (0.062635) with: {'learning_rate': 0.06, 'max_depth': 1, 'n_estimators': 100}
0.790555 (0.063551) with: {'learning_rate': 0.06, 'max_depth': 1, 'n_estimators': 150}
0.795161 (0.067328) with: {'learning_rate': 0.06, 'max_depth': 1, 'n_estimators': 200}
0.850889 (0.032347) with: {'learning_rate': 0.06, 'max_depth': 3, 'n_estimators': 50}
0.867786 (0.034764) with: {'learning_rate': 0.06, 'max_depth': 3, 'n_estimators': 100}
0.870313 (0.035557) with: {'learning_rate': 0.06, 'max_depth': 3, 'n_estimators': 150}
0.870957 (0.036189) with: {'learning_rate': 0.06, 'max_depth': 3, 'n_estimators': 200}
0.850339 (0.038543) with: {'learning_rate': 0.06, 'max_depth': 5, 'n_estimators': 50}
0.864939 (0.034315) with: {'learning_rate': 0.06, 'max_depth': 5, 'n_estimators': 100}
0.865762 (0.033280) with: {'learning_rate': 0.06, 'max_depth': 5, 'n_estimators': 150}
0.865658 (0.032928) with: {'learning_rate': 0.06, 'max_depth': 5, 'n_estimators': 200}
0.839195 (0.034252) with: {'learning_rate': 0.06, 'max_depth': 7, 'n_estimators': 50}
0.852720 (0.027235) with: {'learning_rate': 0.06, 'max_depth': 7, 'n_estimators': 100}
0.853702 (0.027123) with: {'learning_rate': 0.06, 'max_depth': 7, 'n_estimators': 150}
0.853363 (0.026771) with: {'learning_rate': 0.06, 'max_depth': 7, 'n_estimators': 200}
0.835426 (0.038498) with: {'learning_rate': 0.06, 'max_depth': 9, 'n_estimators': 50}
0.850673 (0.029506) with: {'learning_rate': 0.06, 'max_depth': 9, 'n_estimators': 100}
0.851901 (0.028517) with: {'learning_rate': 0.06, 'max_depth': 9, 'n_estimators': 150}
0.852140 (0.028341) with: {'learning_rate': 0.06, 'max_depth': 9, 'n_estimators': 200}
0.830953 (0.038574) with: {'learning_rate': 0.06, 'max_depth': 11, 'n_estimators': 50}
0.843297 (0.032231) with: {'learning_rate': 0.06, 'max_depth': 11, 'n_estimators': 100}
0.844927 (0.031055) with: {'learning_rate': 0.06, 'max_depth': 11, 'n_estimators': 150}
0.845079 (0.030915) with: {'learning_rate': 0.06, 'max_depth': 11, 'n_estimators': 200}
0.832709 (0.036911) with: {'learning_rate': 0.06, 'max_depth': 13, 'n_estimators': 50}
0.844690 (0.029680) with: {'learning_rate': 0.06, 'max_depth': 13, 'n_estimators': 100}
0.846225 (0.028501) with: {'learning_rate': 0.06, 'max_depth': 13, 'n_estimators': 150}
0.846421 (0.028346) with: {'learning_rate': 0.06, 'max_depth': 13, 'n_estimators': 200}
0.832991 (0.037219) with: {'learning_rate': 0.06, 'max_depth': 15, 'n_estimators': 50}
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0.847907 (0.026163) with: {'learning_rate': 0.06, 'max_depth': 15, 'n_estimators': 150}
0.848179 (0.025976) with: {'learning_rate': 0.06, 'max_depth': 15, 'n_estimators': 200}
0.769494 (0.058840) with: {'learning_rate': 0.09, 'max_depth': 1, 'n_estimators': 50}
0.791904 (0.061566) with: {'learning_rate': 0.09, 'max_depth': 1, 'n_estimators': 100}
0.798994 (0.066574) with: {'learning_rate': 0.09, 'max_depth': 1, 'n_estimators': 150}
0.803098 (0.068958) with: {'learning_rate': 0.09, 'max_depth': 1, 'n_estimators': 200}
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0.828890 (0.049491) with: {'learning_rate': 0.3, 'max_depth': 15, 'n_estimators': 100}
0.828890 (0.049491) with: {'learning_rate': 0.3, 'max_depth': 15, 'n_estimators': 150}
0.828890 (0.049491) with: {'learning_rate': 0.3, 'max_depth': 15, 'n_estimators': 200}

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