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sklearn.experimental.enable_iterative_imputer
激活迭代输入。 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.impute.IterativeImputer`](https:
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sklearn.multiclass.OutputCodeClassifier
```python class sklearn.multiclass.OutputCodeClassifier(estimator, *, code_size=1.5, random_state=No
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sklearn.multioutput.RegressorChain
```python class sklearn.multioutput.RegressorChain(base_estimator, *, order=None, cv=None, random_st
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sklearn.naive_bayes.MultinomialNB
```python class sklearn.naive_bayes.MultinomialNB(*, alpha=1.0, fit_prior=True, class_prior=None)
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sklearn.neighbors.radius_neighbors_graph
``` sklearn.neighbors.radius_neighbors_graph(X, radius, *, mode='connectivity', metric='minkowski',
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sklearn.neural_network.MLPRegressor
```python class sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(100, ), activation='relu', *
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sklearn.pipeline.make_union
```python sklearn.pipeline.make_union(*transformers, **kwargs) ``` [[源码](https://github.com/sci
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sklearn.feature_extraction.TfidfVectorizer
```python class sklearn.feature_extraction.text.TfidfVectorizer(*, input='content', encoding='utf-8
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sklearn.feature_selection.mutual_info_regression
```python sklearn.feature_selection.mutual_info_regression(X, y, *, discrete_features='auto', n_nei
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sklearn.preprocessing.power_transform
```python sklearn.preprocessing.power_transform(X, method='yeo-johnson', *, standardize=True, copy=
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sklearn.impute.KNNImputer
```python class sklearn.impute.KNNImputer(*, missing_values=nan, n_neighbors=5, weights='uniform',
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sklearn.random_projection.johnson_lindenstrauss_min_dim
``` sklearn.random_projection.johnson_lindenstrauss_min_dim(n_samples, *, eps=0.1) ``` [[源码\]](
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sklearn.semi_supervised.LabelSpreading
```python class sklearn.semi_supervised.LabelSpreading(kernel='rbf', *, gamma=20, n_neighbors=7, al
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sklearn.svm.l1_min_c
```python sklearn.svm.l1_min_c(X, y, *, loss='squared_hinge', fit_intercept=True, intercept_scaling=
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sklearn.tree.plot_tree
```python sklearn.tree.plot_tree(decision_tree, *, max_depth=None, feature_names=None, class_names=N
