自動化機器學習(Auto-ML)是指數據科學模型開發的管道組件自動化。AutoML 減少了數據科學家的工作量并加快了工作流程。AutoML 可用于自動化各種管道組件，包括數據理解，EDA，數據處理，模型訓練，超參數調整等。

對于端到端機器學習項目，每個組件的復雜性取決于項目。我們知道市面上有很多的 AutoML 開源庫可加快開發的速度。在本文中，我將分享一個非常棒的python工具庫「LazyPredict」。

什么是LazyPredict？

LazyPredict是一個開源Python庫，可自動執行模型訓練管道并加快工作流程。LazyPredict可以為分類數據集訓練約30個分類模型，為回歸數據集訓練約40個回歸模型。

LazyPredict將返回經過訓練的模型以及其性能指標，而無需編寫太多代碼。可以輕松比較每個模型的性能指標，并調整最佳模型以進一步提高性能。

安裝

可以使用以下方法從PyPl庫中安裝LazyPredict：

pip?install?lazypredict

安裝后，可以導入庫以執行分類和回歸模型的自動訓練。

from?lazypredict.Supervised?import?LazyRegressor,?LazyClassifier

用法

LazyPredict 同時支持分類和回歸問題，因此我將利用案例說明：波士頓住房（回歸）和泰坦尼克號（分類）數據集用于LazyPredict庫的演示。

分類任務

LazyPredict 的用法非常直觀，類似于scikit-learn。首先為分類任務創建一個估計器 LazyClassifier 的實例，可以通過自定義指標進行評估，默認情況下，每個模型都將根據準確性，ROC、AUC得分， F1-score進行評估。

在進行 lazypredict 模型訓練之前，必須先讀取數據集并進行處理，以使其適合訓練。在進行特征工程并將數據拆分為訓練測試數據之后，我們可以使用 LazyPredict 進行模型訓練。

#?LazyClassifier?Instance?and?fiting?data cls=?LazyClassifier(ignore_warnings=False,?custom_metric=None) models,?predictions?=?cls.fit(X_train,?X_test,?y_train,?y_test)

回歸任務

與分類模型訓練相似，LazyPredict附帶了針對回歸數據集的自動模型訓練。該實現類似于分類任務，只是實例LazyRegressor有所更改。

reg?=?LazyRegressor(ignore_warnings=False,?custom_metric=None) models,?predictions?=?reg.fit(X_train,?X_test,?y_train,?y_test)

觀察以上性能指標，AdaBoost分類器是分類任務的最佳表現模型，而GradientBoostingRegressor模型是回歸任務的最佳表現模型。

完整版案例

分類

from?lazypredict.Supervised?import?LazyClassifier from?sklearn.datasets?import?load_breast_cancer from?sklearn.model_selection?import?train_test_splitdata?=?load_breast_cancer() X?=?data.data y=?data.targetX_train,?X_test,?y_train,?y_test?=?train_test_split(X,?y,test_size=.5,random_state?=123)clf?=?LazyClassifier(verbose=0,ignore_warnings=True,?custom_metric=None) models,predictions?=?clf.fit(X_train,?X_test,?y_train,?y_test)print(models)|?Model??????????????????????????|???Accuracy?|???Balanced?Accuracy?|???ROC?AUC?|???F1?Score?|???Time?Taken?| |:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:| |?LinearSVC??????????????????????|???0.989474?|????????????0.987544?|??0.987544?|???0.989462?|????0.0150008?| |?SGDClassifier??????????????????|???0.989474?|????????????0.987544?|??0.987544?|???0.989462?|????0.0109992?| |?MLPClassifier??????????????????|???0.985965?|????????????0.986904?|??0.986904?|???0.985994?|????0.426?????| |?Perceptron?????????????????????|???0.985965?|????????????0.984797?|??0.984797?|???0.985965?|????0.0120046?| |?LogisticRegression?????????????|???0.985965?|????????????0.98269??|??0.98269??|???0.985934?|????0.0200036?| |?LogisticRegressionCV???????????|???0.985965?|????????????0.98269??|??0.98269??|???0.985934?|????0.262997??| |?SVC????????????????????????????|???0.982456?|????????????0.979942?|??0.979942?|???0.982437?|????0.0140011?| |?CalibratedClassifierCV?????????|???0.982456?|????????????0.975728?|??0.975728?|???0.982357?|????0.0350015?| |?PassiveAggressiveClassifier????|???0.975439?|????????????0.974448?|??0.974448?|???0.975464?|????0.0130005?| |?LabelPropagation???????????????|???0.975439?|????????????0.974448?|??0.974448?|???0.975464?|????0.0429988?| |?LabelSpreading?????????????????|???0.975439?|????????????0.974448?|??0.974448?|???0.975464?|????0.0310006?| |?RandomForestClassifier?????????|???0.97193??|????????????0.969594?|??0.969594?|???0.97193??|????0.033?????| |?GradientBoostingClassifier?????|???0.97193??|????????????0.967486?|??0.967486?|???0.971869?|????0.166998??| |?QuadraticDiscriminantAnalysis??|???0.964912?|????????????0.966206?|??0.966206?|???0.965052?|????0.0119994?| |?HistGradientBoostingClassifier?|???0.968421?|????????????0.964739?|??0.964739?|???0.968387?|????0.682003??| |?RidgeClassifierCV??????????????|???0.97193??|????????????0.963272?|??0.963272?|???0.971736?|????0.0130029?| |?RidgeClassifier????????????????|???0.968421?|????????????0.960525?|??0.960525?|???0.968242?|????0.0119977?| |?AdaBoostClassifier?????????????|???0.961404?|????????????0.959245?|??0.959245?|???0.961444?|????0.204998??| |?ExtraTreesClassifier???????????|???0.961404?|????????????0.957138?|??0.957138?|???0.961362?|????0.0270066?| |?KNeighborsClassifier???????????|???0.961404?|????????????0.95503??|??0.95503??|???0.961276?|????0.0560005?| |?BaggingClassifier??????????????|???0.947368?|????????????0.954577?|??0.954577?|???0.947882?|????0.0559971?| |?BernoulliNB????????????????????|???0.950877?|????????????0.951003?|??0.951003?|???0.951072?|????0.0169988?| |?LinearDiscriminantAnalysis?????|???0.961404?|????????????0.950816?|??0.950816?|???0.961089?|????0.0199995?| |?GaussianNB?????????????????????|???0.954386?|????????????0.949536?|??0.949536?|???0.954337?|????0.0139935?| |?NuSVC??????????????????????????|???0.954386?|????????????0.943215?|??0.943215?|???0.954014?|????0.019989??| |?DecisionTreeClassifier?????????|???0.936842?|????????????0.933693?|??0.933693?|???0.936971?|????0.0170023?| |?NearestCentroid????????????????|???0.947368?|????????????0.933506?|??0.933506?|???0.946801?|????0.0160074?| |?ExtraTreeClassifier????????????|???0.922807?|????????????0.912168?|??0.912168?|???0.922462?|????0.0109999?| |?CheckingClassifier?????????????|???0.361404?|????????????0.5??????|??0.5??????|???0.191879?|????0.0170043?| |?DummyClassifier????????????????|???0.512281?|????????????0.489598?|??0.489598?|???0.518924?|????0.0119965?|

回歸

from?lazypredict.Supervised?import?LazyRegressor from?sklearn?import?datasets from?sklearn.utils?import?shuffle import?numpy?as?npboston?=?datasets.load_boston() X,?y?=?shuffle(boston.data,?boston.target,?random_state=13) X?=?X.astype(np.float32)offset?=?int(X.shape[0]?*?0.9)X_train,?y_train?=?X[:offset],?y[:offset] X_test,?y_test?=?X[offset:],?y[offset:]reg?=?LazyRegressor(verbose=0,?ignore_warnings=False,?custom_metric=None) models,?predictions?=?reg.fit(X_train,?X_test,?y_train,?y_test)print(models)|?Model?????????????????????????|?Adjusted?R-Squared?|?R-Squared?|??RMSE?|?Time?Taken?| |:------------------------------|-------------------:|----------:|------:|-----------:| |?SVR???????????????????????????|???????????????0.83?|??????0.88?|??2.62?|???????0.01?| |?BaggingRegressor??????????????|???????????????0.83?|??????0.88?|??2.63?|???????0.03?| |?NuSVR?????????????????????????|???????????????0.82?|??????0.86?|??2.76?|???????0.03?| |?RandomForestRegressor?????????|???????????????0.81?|??????0.86?|??2.78?|???????0.21?| |?XGBRegressor??????????????????|???????????????0.81?|??????0.86?|??2.79?|???????0.06?| |?GradientBoostingRegressor?????|???????????????0.81?|??????0.86?|??2.84?|???????0.11?| |?ExtraTreesRegressor???????????|???????????????0.79?|??????0.84?|??2.98?|???????0.12?| |?AdaBoostRegressor?????????????|???????????????0.78?|??????0.83?|??3.04?|???????0.07?| |?HistGradientBoostingRegressor?|???????????????0.77?|??????0.83?|??3.06?|???????0.17?| |?PoissonRegressor??????????????|???????????????0.77?|??????0.83?|??3.11?|???????0.01?| |?LGBMRegressor?????????????????|???????????????0.77?|??????0.83?|??3.11?|???????0.07?| |?KNeighborsRegressor???????????|???????????????0.77?|??????0.83?|??3.12?|???????0.01?| |?DecisionTreeRegressor?????????|???????????????0.65?|??????0.74?|??3.79?|???????0.01?| |?MLPRegressor??????????????????|???????????????0.65?|??????0.74?|??3.80?|???????1.63?| |?HuberRegressor????????????????|???????????????0.64?|??????0.74?|??3.84?|???????0.01?| |?GammaRegressor????????????????|???????????????0.64?|??????0.73?|??3.88?|???????0.01?| |?LinearSVR?????????????????????|???????????????0.62?|??????0.72?|??3.96?|???????0.01?| |?RidgeCV???????????????????????|???????????????0.62?|??????0.72?|??3.97?|???????0.01?| |?BayesianRidge?????????????????|???????????????0.62?|??????0.72?|??3.97?|???????0.01?| |?Ridge?????????????????????????|???????????????0.62?|??????0.72?|??3.97?|???????0.01?| |?TransformedTargetRegressor????|???????????????0.62?|??????0.72?|??3.97?|???????0.01?| |?LinearRegression??????????????|???????????????0.62?|??????0.72?|??3.97?|???????0.01?| |?ElasticNetCV??????????????????|???????????????0.62?|??????0.72?|??3.98?|???????0.04?| |?LassoCV???????????????????????|???????????????0.62?|??????0.72?|??3.98?|???????0.06?| |?LassoLarsIC???????????????????|???????????????0.62?|??????0.72?|??3.98?|???????0.01?| |?LassoLarsCV???????????????????|???????????????0.62?|??????0.72?|??3.98?|???????0.02?| |?Lars??????????????????????????|???????????????0.61?|??????0.72?|??3.99?|???????0.01?| |?LarsCV????????????????????????|???????????????0.61?|??????0.71?|??4.02?|???????0.04?| |?SGDRegressor??????????????????|???????????????0.60?|??????0.70?|??4.07?|???????0.01?| |?TweedieRegressor??????????????|???????????????0.59?|??????0.70?|??4.12?|???????0.01?| |?GeneralizedLinearRegressor????|???????????????0.59?|??????0.70?|??4.12?|???????0.01?| |?ElasticNet????????????????????|???????????????0.58?|??????0.69?|??4.16?|???????0.01?| |?Lasso?????????????????????????|???????????????0.54?|??????0.66?|??4.35?|???????0.02?| |?RANSACRegressor???????????????|???????????????0.53?|??????0.65?|??4.41?|???????0.04?| |?OrthogonalMatchingPursuitCV???|???????????????0.45?|??????0.59?|??4.78?|???????0.02?| |?PassiveAggressiveRegressor????|???????????????0.37?|??????0.54?|??5.09?|???????0.01?| |?GaussianProcessRegressor??????|???????????????0.23?|??????0.43?|??5.65?|???????0.03?| |?OrthogonalMatchingPursuit?????|???????????????0.16?|??????0.38?|??5.89?|???????0.01?| |?ExtraTreeRegressor????????????|???????????????0.08?|??????0.32?|??6.17?|???????0.01?| |?DummyRegressor????????????????|??????????????-0.38?|?????-0.02?|??7.56?|???????0.01?| |?LassoLars?????????????????????|??????????????-0.38?|?????-0.02?|??7.56?|???????0.01?| |?KernelRidge???????????????????|?????????????-11.50?|?????-8.25?|?22.74?|???????0.01?|

結論

在本文中，我們討論了LazyPredict庫的實現，該庫可以在幾行Python代碼中訓練大約70個分類和回歸模型。這是一個非常方便的工具，因為它提供了模型執行的總體情況，并且可以比較每個模型的性能。

每個模型都使用其默認參數進行訓練，因為它不執行超參數調整。選擇性能最佳的模型后，開發人員可以調整模型以進一步提高性能。

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