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sklearn.naive_bayes.ComplementNB
```python class sklearn.naive_bayes.ComplementNB（*，alpha = 1.0，fit_prior = True，class_prior = None，
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sklearn.neighbors.NeighborhoodComponentsAnalysis
``` class sklearn.neighbors.NeighborhoodComponentsAnalysis(n_components=None, *, init='auto', warm_s
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sklearn.neural_network.BernoulliRB
```python class sklearn.neural_network.BernoulliRBM(n_components=256, *, learning_rate=0.1, batch_s
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sklearn.pipeline.Pipeline
```python class sklearn.pipeline.Pipeline(steps, *, memory=None, verbose=False) ``` [[源码](https://g
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sklearn.feature_extraction.HashingVectorizer
```python class sklearn.feature_extraction.text.HashingVectorizer(*, input='content', encoding='utf
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sklearn.feature_selection.f_regression
```python sklearn.feature_selection.f_regression(X, y, *, center=True) ``` [[源码]](https://githu
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sklearn.preprocessing.robust_scale
```python sklearn.preprocessing.robust_scale(X, *, axis=0, with_centering=True, with_scaling=True,
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sklearn.impute.MissingIndicator
```python class sklearn.impute.MissingIndicator(*, missing_values=nan, features='missing-only', spa
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sklearn.random_projection.GaussianRandomProjection
``` class sklearn.random_projection.GaussianRandomProjection(n_components='auto', *, eps=0.1, random
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sklearn.svm.SVC
```python class sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinkin
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sklearn.tree.export_graphviz
```python sklearn.tree.export_graphviz(decision_tree, out_file=None, *, max_depth=None, feature_nam
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sklearn.utils.all_estimators
```python sklearn.utils.all_estimators(type_filter=None) ``` [源码](https://github.com/scikit-learn/s
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sklearn.inspection.permutation_importance
```python sklearn.inspection.permutation_importance(estimator, X, y, *, scoring=None, n_repeats=5, n
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sklearn.isotonic.IsotonicRegression
阅读更多内容[用户指南](http://scikit-learn.jg.com.cn/view/103.html). *新版本0.13。* | 参数 | 说明
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处理文本数据
本指南的目的是在一项实际任务上探索一些主要的scikit学习工具：分析有关二十个不同主题的文本文档（新闻组帖子）的集合。 在本节中，我们将看到如何： - 加载文件内容和类别 - 提取适合机器学习
