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• sklearn.metrics.PrecisionRecallDisplay
  ```python class sklearn.metrics.PrecisionRecallDisplay(precision, recall, *, average_precision=None,
• sklearn.datasets.make_swiss_roll
  ```python sklearn.datasets.make_swiss_roll(n_samples=100, *, noise=0.0, random_state=None) ```
• sklearn.discriminant_analysis.LinearDiscriminantAnalysis
  ```python class sklearn.discriminant_analysis.LinearDiscriminantAnalysis(*, solver='svd', shrinkage
• sklearn.dummy.DummyClassifier
  ```python class sklearn.dummy.DummyClassifier(*, strategy='warn', random_state=None, constant=None)
• sklearn.mixture.BayesianGaussianMixture
  ```python class sklearn.mixture.BayesianGaussianMixture(*, n_components=1, covariance_type='full',
• sklearn.ensemble.HistGradientBoostingRegressor
  ```python class sklearn.ensemble.HistGradientBoostingRegressor(loss='least_squares', *, learning_ra
• sklearn.model_selection.permutation_test_score
  ```python sklearn.model_selection.permutation_test_score(estimator, X, y, *, groups=None, cv=None, n
• sklearn.exceptionsNonBLASDotWarning
  ```python class sklearn.exceptions.NonBLASDotWarning ``` [[源码]](https://github.com/scikit-learn
• sklearn.experimental.enable_hist_gradient_boosting
  启用基于直方图的梯度增强估计器。 这些评估器的API和结果可能会在没有任何弃用周期的情况下发生变化。 动态导入该文件将设置[`sklearn.ensemble.HistGradientBo
• sklearn.multiclass.OneVsOneClassifier
  ```python class sklearn.multiclass.OneVsOneClassifier(estimator, *, n_jobs=None) ``` [[源码\]](https:
• sklearn.multioutput.MultiOutputClassifier
  ```python class sklearn.multioutput.MultiOutputClassifier(estimator, *, n_jobs=None) ``` [[源码\]](ht
• sklearn.naive_bayes.GaussianNB
  ```python class sklearn.naive_bayes.GaussianNB(*, priors=None, var_smoothing=1e-09) ``` [[源码](https
• sklearn.neighbors.kneighbors_graph
  ``` sklearn.neighbors.kneighbors_graph(X, n_neighbors, *, mode='connectivity', metric='minkowski',
• sklearn.neural_network.MLPClassifier
  ```python class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100, ), activation='relu',
• sklearn.pipeline.make_pipeline
  ```python sklearn.pipeline.make_pipeline(*steps, **kwargs) ``` [[源码](https://github.com/scikit-

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