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sklearn.utils.multiclass.type_of_target
```Python sklearn.utils.multiclass.type_of_target(y) ``` [源码](https://github.com/scikit-learn/s
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均值移位聚类算法的一个例子
> 参考： > > Dorin Comaniciu and Peter Meer, “Mean Shift: A robust approach toward feature space anal
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Logistic回归三分类器
下面显示的是一个逻辑回归分类器，在[iris](https://en.wikipedia.org/wiki/Iris_flower_data_set)数据集的前两个维度(萼片长度和宽度)上的决策边界。
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sklearn.linear_model.LassoCV
```python class sklearn.linear_model.LassoCV(*, eps=0.001, n_alphas=100, alphas=None, fit_intercept=
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sklearn.datasets.load_sample_image
```python sklearn.datasets.load_sample_image(image_name) ``` [[源码]](https://github.com/scikit-l
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sklearn.metrics.pairwise.distance_metrics
```python sklearn.metrics.pairwise.distance_metrics() ``` [源码](https://github.com/scikit-learn/
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sklearn.model_selection.GroupKFold
```python class sklearn.model_selection.GroupKFold(n_splits=5) ``` [[源码](https://github.com/sci
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sklearn.preprocessing.KBinsDiscretizer
```python class sklearn.preprocessing.KBinsDiscretizer(n_bins=5, *, encode='onehot', strategy='quant
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sklearn.utils.multiclass.is_multilabel
```python sklearn.utils.multiclass.is_multilabel(y) ``` [源码](https://github.com/scikit-learn/sc
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k-均值假设的证明
此示例旨在说明k-means将产生不直观的、可能是意外的聚类的情况。在前三幅图中，输入的数据不符合一些隐含的假设，即k均值生成，因此产生了不理想的聚类。在最后一幅图中，k-means表示尽管大小不均匀
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SGD：加权样本
绘制加权数据集的决策函数， 其中点的大小与其权重成正比。  ```python print(
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sklearn.linear_model.LassoLars
```python class sklearn.linear_model.LassoLars(alpha=1.0, *, fit_intercept=True, verbose=False, norm
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sklearn.datasets.load_sample_images
```python sklearn.datasets.load_sample_images() ``` [[源码]](https://github.com/scikit-learn/scik
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sklearn.metrics.pairwise.euclidean_distances
```python sklearn.metrics.pairwise.euclidean_distances(X, Y=None, *, Y_norm_squared=None, squared=F
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sklearn.model_selection.GroupShuffleSplit
```python class sklearn.model_selection.GroupShuffleSplit(n_splits=5, *, test_size=None, train_size=
