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sklearn.feature_extraction.TfidfTransformer
```python class sklearn.feature_extraction.text.TfidfTransformer(*, norm='l2', use_idf=True, smooth
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sklearn.feature_selection.mutual_info_classif
```python sklearn.feature_selection.mutual_info_classif(X, y, *, discrete_features='auto', n_neighb
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sklearn.preprocessing.scale
```python sklearn.preprocessing.scale(X, *, axis=0, with_mean=True, with_std=True, copy=True) ```
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sklearn.impute.IterativeImputer
```python class sklearn.impute.IterativeImputer(estimator=None, *, missing_values=nan, sample_poste
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sklearn.random_projection.SparseRandomProjection
``` class sklearn.random_projection.SparseRandomProjection(n_components='auto', *, density='auto', e
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sklearn.semi_supervised.LabelPropagation
```python class sklearn.semi_supervised.LabelPropagation(kernel='rbf', *, gamma=20, n_neighbors=7,
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sklearn.svm.SVR
```python class sklearn.svm.SVR(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1
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sklearn.tree.export_text
```python sklearn.tree.export_text(decision_tree, *, feature_names=None, max_depth=10, spacing=3, d
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sklearn.utils.parallel_backend
```python sklearn.utils.parallel_backend(backend, n_jobs=-1, inner_max_num_threads=None, **backend_
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sklearn.model_selection.fit_grid_point
**警告弃用** ```python sklearn.model_selection.fit_grid_point(X, y, estimator, parameters, train, te
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sklearn.inspection.PartialDependenceDisplay
部分依赖图（PDP）可视化。 建议使用 [`plot_partial_dependence`](https://scikit-learn.org.cn/view/851.html)创建一个 [`Pa
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sklearn.isotonic.check_increasing
```python sklearn.isotonic.check_increasing(x, y) ``` [[源码\]](https://github.com/scikit-learn/s
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选择适合的估算器
解决机器学习问题最困难的部分通常是为工作找到合适的估算器。 不同的估算器更适合于不同类型的数据和不同问题。 下面的流程图旨在为用户提供一些粗略的指导，以指导他们如何处理有关哪些估算器尝试使
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sklearn.gaussian_process.Sum
```python class sklearn.gaussian_process.kernels.Sum(k1, k2) ``` [[源码]](https://github.com/scik
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sklearn.decomposition.KernelPCA
``` class sklearn.decomposition.KernelPCA(n_components=None, *, kernel='linear', gamma=None, degree
