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sklearn.decomposition.dict_learning
```python sklearn.decomposition.dict_learning(X, n_components, *, alpha, max_iter=100, tol=1e-08, m
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sklearn.ensemble.RandomTreesEmbedding
```python class sklearn.ensemble.RandomTreesEmbedding(n_estimators=100, *, max_depth=5, min_samples_
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sklearn.model_selection.RandomizedSearchCV
```python class sklearn.model_selection.RandomizedSearchCV（estimator，param_distributions，*，n_iter =
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sklearn.exceptionsDataConversionWarning
```python class sklearn.exceptions.DataConversionWarning ``` [[源码]](https://github.com/scikit-l
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sklearn.feature_extraction.img_to_graph
```python sklearn.feature_extraction.image.img_to_graph(img, *, mask=None, return_as=
- sklearn.neighbors.RadiusNeighborsRegressor
``` class sklearn.neighbors.RadiusNeighborsRegressor(radius=1.0, *, weights='uniform', algorithm='au- sklearn.feature_selection.RFECV
```python class sklearn.feature_selection.RFECV(estimator, *, step=1, min_features_to_select=1, cv=N- sklearn.preprocessing.maxabs_scale
```python sklearn.preprocessing.maxabs_scale(X, *, axis=0, copy=True) ``` [[源码]](https://github- sklearn.svm.LinearSVR
```python class sklearn.svm.LinearSVR(*, epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive',- sklearn.tree.DecisionTreeClassifier
```python class sklearn.tree.DecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=- sklearn.utils.validation.check_memory
```python sklearn.utils.validation.check_memory(memory) ``` [源码](https://github.com/scikit-lear- sklearn.gaussian_process.Matern
```python class sklearn.gaussian_process.kernels.Matern(length_scale=1.0, length_scale_bounds=(1e-0- K-Means和MiniBatchKMeans聚类算法的比较
我们想比较一下MiniBatchKMeans和KMeans的性能：MiniBatchKMeans更快，但给出的结果略有不同(看 [Mini Batch K-Means](https://scikit-- 1.12 多类和多标签算法
> **警告** scikit-learn中的所有分类器都可以开箱即用进行多分类。除非您想尝试不同的多类策略，否则无需使用[`sklearn.multiclass`](http://scikit-le- 2.4. 双聚类
可以使用`sklearn.cluster.bicluster`模块进行Biclustering(双聚类) 。 双聚类算法同时对数据矩阵的行和列进行聚类。而这些行列的聚类称之为双向簇(bicluster - sklearn.neighbors.RadiusNeighborsRegressor
