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sklearn.cluster.KMeans
```python class sklearn.cluster.KMeans(n_clusters=8, *, init='k-means++', n_init=10, max_iter=300,
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sklearn.linear_model.MultiTaskElasticNetCV
```python class sklearn.linear_model.MultiTaskElasticNetCV(*, l1_ratio=0.5, eps=0.001, n_alphas=100,
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sklearn.metrics.pairwise.polynomial_kernel
```python sklearn.metrics.pairwise.polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1) ```
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sklearn.datasets.make_classification
```python sklearn.datasets.make_classification(n_samples=100, n_features=20, *, n_informative=2, n_r
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sklearn.decomposition.FactorAnalysis
```python class sklearn.decomposition.FactorAnalysis(n_components=None, *, tol=0.01, copy=True, max_
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sklearn.ensemble.AdaBoostRegressor
```python class sklearn.ensemble.AdaBoostRegressor(base_estimator=None, *, n_estimators=50, learning
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sklearn.model_selection.RepeatedStratifiedKFold
```python class sklearn.model_selection.RepeatedStratifiedKFold(*, n_splits=5, n_repeats=10, random
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sklearn.preprocessing.OneHotEncoder
```python class sklearn.preprocessing.OneHotEncoder(*, categories='auto', drop=None, sparse=True, dt
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sklearn.utils.sparsefuncs.inplace_column_scale
```Python sklearn.utils.sparsefuncs.inplace_column_scale(X, scale) ``` [源码](https://github.com/
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基于硬币图像的结构化区域分层聚类演示
基于区域层次聚类的二维图像分割计算。聚类在空间上受到约束，以使每个分割区域成为一体。  `
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1.2 线性和二次判别分析
线性判别分析([`discriminant_analysis.LinearDiscriminantAnalysis`](https://scikit-learn.org.cn/view/618.htm
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随机森林与多输出元估计器的比较
随机森林的多输出回归器和[multioutput.MultiOutputRegressor](https://scikit-learn.org.cn/view/91.html)元估计器的比较。 这个
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在iris数据集上绘制多类SGD
在iris数据集上绘制多类SGD的决策边界。对应于三个one-versus-all(OVA)分类器的超平面用虚线表示。 
