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sklearn.naive_bayes.CategoricalNB
```python class sklearn.naive_bayes.CategoricalNB(*, alpha=1.0, fit_prior=True, class_prior=None)
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sklearn.neighbors.NearestNeighbors
``` class sklearn.neighbors.NearestNeighbors(*, n_neighbors=5, radius=1.0, algorithm='auto', leaf_si
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sklearn.pipeline.FeatureUnion
```python class sklearn.pipeline.FeatureUnion(transformer_list, *, n_jobs=None, transformer_weights=
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sklearn.feature_extraction.CountVectorizer
```python class sklearn.feature_extraction.text.CountVectorizer(*, input='content', encoding='utf-8
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sklearn.feature_selection.f_classif
```python sklearn.feature_selection.f_classif(X, y) ``` [[源码]](https://github.com/scikit-learn/
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sklearn.impute.SimpleImputer
```python class sklearn.impute.SimpleImputer(*, missing_values=nan, strategy='mean', fill_value=None
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sklearn.preprocessing.quantile_transform
```python sklearn.preprocessing.quantile_transform(X, *, axis=0, n_quantiles=1000, output_distribut
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sklearn.svm.OneClassSVM
```python class sklearn.svm.OneClassSVM(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0
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sklearn.tree.ExtraTreeRegressor
```python class sklearn.tree.ExtraTreeRegressor(*, criterion='mse', splitter='random', max_depth=Non
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sklearn.utils.validation.has_fit_parameter
```python sklearn.utils.validation.has_fit_parameter(estimator, parameter) ``` [源码](https://gi
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sklearn.inspection.partial_dependence
```python sklearn.inspection.partial_dependence(estimator, X, features, *, response_method='auto',
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科学数据处理统计学习指南
**统计学习** 机器学习是一项越来越重要的技术，因为实验科学面临的数据集规模正在迅速增长。 它所解决的问题包括建立链接不同观察值的预测功能，对观察值进行分类或学习未标记数据集中的结构。 本教程将
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sklearn.gaussian_process.RBF
```python class sklearn.gaussian_process.kernels.RBF(length_scale=1.0, length_scale_bounds=(1e-05, 1
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谱协聚类算法的一个示例
注意 单击[此处](https://scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html#sp
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概率校准曲线
在进行分类时，不仅要预测类标签，还要预测相关概率。这种概率给了预测某种程度的信心。此示例演示了如何显示预测概率的校准效果，以及如何校准未校准的分类器。 实验是在一个二分类的人工数据集上进行的，该数据
