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)

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

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}

0.850041 (0.025481) with: {'learning_rate': 0.03, 'max_depth': 7, 'n_estimators': 200}

0.757383 (0.050920) with: {'learning_rate': 0.03, 'max_depth': 9, 'n_estimators': 50}

0.834539 (0.037476) with: {'learning_rate': 0.03, 'max_depth': 9, 'n_estimators': 100}

0.845794 (0.033418) with: {'learning_rate': 0.03, 'max_depth': 9, 'n_estimators': 150}

0.848075 (0.032044) with: {'learning_rate': 0.03, 'max_depth': 9, 'n_estimators': 200}

0.754782 (0.053572) with: {'learning_rate': 0.03, 'max_depth': 11, 'n_estimators': 50}

0.831093 (0.039371) with: {'learning_rate': 0.03, 'max_depth': 11, 'n_estimators': 100}

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}

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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}

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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}

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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}

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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}

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0.798994 (0.066574) with: {'learning_rate': 0.09, 'max_depth': 1, 'n_estimators': 150}

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0.866892 (0.042026) with: {'learning_rate': 0.09, 'max_depth': 3, 'n_estimators': 150}

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