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  • metrics.pairwise.manhattan_distances
    ```python sklearn.metrics.pairwise.manhattan_distances(X, Y=None, *, sum_over_features=True) ```
  • sklearn.model_selection.LeavePOut
    ```python class sklearn.model_selection.LeavePOut(p) ``` [[源码](https://github.com/scikit-learn/
  • sklearn.preprocessing.MaxAbsScaler
    ```python class sklearn.preprocessing.MaxAbsScaler(*, copy=True) ``` [[源码]](https://github.com/scik
  • sklearn.utils.safe_sqr
    ```python sklearn.utils.safe_sqr(X, *, copy=True) ``` [源码](https://github.com/scikit-learn/scik
  • 二维数字嵌入上的各种凝聚聚类
    在数字数据集的2D嵌入上用于聚集聚类的各种链接选项的说明。 此示例的目标是直观地显示指标的行为，而不是为数字找到好的聚类。这就是为什么这个例子适用于2D嵌入。 这个例子向我们展示的是聚集性
  • 单调约束
    这个例子说明了单调约束对梯度提升估计器的影响。 我们创建了一个人工数据集，其中目标值一般与第一个特征正相关(具有一些随机和非随机变化)，而在一般情况下与第二个特征呈负相关。 通过在学习过程
  • 比较各种在线求解器
    一个示例，展示了不同的在线求解器在手写数字数据集上的表现。 ![](/upload/6b63ed5244fddddaa7d71f48ea635233.png) ```python train
  • sklearn.cluster.DBSCAN
    ```python class sklearn.cluster.DBSCAN(eps=0.5, *, min_samples=5, metric='euclidean', metric_params
  • sklearn.linear_model.BayesianRidge
    ```python class sklearn.linear_model.BayesianRidge(*, n_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=
  • sklearn.metrics.pairwise.nan_euclidean_distances
    ```python sklearn.metrics.pairwise.nan_euclidean_distances(X, Y=None, *, squared=False, missing_valu
  • sklearn.datasets.make_checkerboard
    ```python sklearn.datasets.make_checkerboard(shape, n_clusters, *, noise=0.0, minval=10, maxval=100,
  • sklearn.model_selection.PredefinedSplit
    ```python class sklearn.model_selection.PredefinedSplit(test_fold) ``` [[源码](https://github.com
  • sklearn.preprocessing.MinMaxScaler
    ```python class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), *, copy=True) ``` 通过将每个要素缩
  • sklearn.utils.shuffle
    ```python sklearn.utils.shuffle(*arrays, **options) ``` [源码](https://github.com/scikit-learn/scikit
  • K-means聚类
    图中首先显示了使用一个K-means算法产生三个聚类会是什么样的结果。然后显示错误初始化对分类过程的影响。通过将n_init设置为1(默认值为10)，可以减少算法使用不同的质心种子运行的次数。下一幅图

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