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sklearn.metrics.pairwise.kernel_metrics
```python sklearn.metrics.pairwise.kernel_metrics() ``` [源码](https://github.com/scikit-learn/sc
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sklearn.model_selection.LeaveOneGroupOut
```python class sklearn.model_selection.LeaveOneGroupOut ``` [[源码](https://github.com/scikit-le
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sklearn.preprocessing.LabelBinarizer
```python class sklearn.preprocessing.LabelBinarizer(*, neg_label=0, pos_label=1, sparse_output=Fals
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sklearn.utils.resample
```python sklearn.utils.resample(*arrays, **options) ``` [源码](https://github.com/scikit-learn/sciki
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亲密传播聚类算法的例子
> 参考：Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Scienc
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AdaBoost决策树回归
使用AdaBoost提升决策树。在一维正弦数据集上的R2[1]算法，具有少量高斯噪声。299个增强(300个决策树)与一个决策树回归器进行比较。随着提升次数的增加，回归器可以适应更多的细节。 > [
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稀疏示例：仅适合特征1和2
糖尿病数据集的特征1和2如下所示。它说明，虽然特征2在整个模型上有很强的系数，但与仅仅特征1相比，它并没有给我们多少关于y的看法。  ``` [[源码]](https://gi
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sklearn.metrics.pairwise.laplacian_kernel
```python sklearn.metrics.pairwise.laplacian_kernel(X, Y=None, gamma=None) ``` [源码](https://git
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sklearn.model_selection.LeavePGroupsOut
```python class sklearn.model_selection.LeavePGroupsOut(n_groups) ``` [[源码](https://github.com/scik
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sklearn.preprocessing.LabelEncoder
```python class sklearn.preprocessing.LabelEncoder ``` [[源码]](https://github.com/scikit-learn/sciki
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sklearn.utils._safe_indexing
```python sklearn.utils._safe_indexing(X, indices, *, axis=0 ``` [源码](https://github.com/sciki
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带结构和无结构的凝聚聚类
此示例显示了强制设置连接图以捕获数据中的本地结构的效果。这张图就是20个最近邻的图。 可以看到强制连接的两个结果。第一个有连接矩阵的聚类要快的多。 第二，当使用一个连接矩阵时，单个、平均和
