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  • sklearn.base.BiclusterMixin
    ```python class sklearn.base.BiclusterMixin ``` [[源码]](https://github.com/scikit-learn/scikit-learn
  • 1.4 支持向量机
    支持向量机(SVMs)是一种用于[分类](http://scikit-learn.jg.com.cn/view/83.html#1.4.1%20%E5%88%86%E7%B1%BB)、[回归](htt
  • 梯度提升正则
    不同正则化策略对梯度提升效果的说明。这个例子来源于 Hastie et al 2009 [1]。 所使用的损失函数是二项偏差。通过收缩进行正则化(`learning_rate < 1.0`)大大提高
  • 稀疏信号上的LASSO与弹性网
    估计Lasso和弹性网络回归对手动产生的、被破坏的、加了噪声的稀疏信号进行建模。估计系数与真实值作了比较。 ![](/upload/286c458a13abef1457abf0da6c8229e
  • 绘制验证曲线
    在此图中，你可以看到针对核参数gamma的不同值的SVM的训练得分和验证得分。 你可以看到，对于非常低的gamma值，训练分数和验证分数都较低，这被称为欠拟合。中等的伽玛值将导致两个得分都较高，即分类
  • 支持向量机：最大边际分割超平面
    本案例绘制了二分类时追求最大边际的分割超平面，使用的算法是线性核下的支持向量机分类器。 ![](https://scikit-learn.org/stable/_images/sphx_glr_
  • sklearn.cluster.OPTICS
    ```python class sklearn.cluster.OPTICS(*, min_samples=5, max_eps=inf, metric='minkowski', p=2, metri
  • sklearn.linear_model.HuberRegressor
    ```python class sklearn.linear_model.HuberRegressor(*, epsilon=1.35, max_iter=100, alpha=0.0001, war
  • sklearn.metrics.pairwise.paired_euclidean_distances
    ```python sklearn.metrics.pairwise.paired_euclidean_distances(X, Y) ``` [源码](https://github.com
  • sklearn.datasets.make_friedman3
    ```python sklearn.datasets.make_friedman3(n_samples=100, *, noise=0.0, random_state=None)[source]
  • sklearn.decomposition.LatentDirichletAllocation
    ```python class sklearn.decomposition.LatentDirichletAllocation(n_components=10, *, doc_topic_prior=
  • sklearn.ensemble.ExtraTreesClassifier
    ```python class sklearn.ensemble.ExtraTreesClassifier(n_estimators=100, *, criterion='gini', max_dep
  • sklearn.model_selection.StratifiedShuffleSplit
    ```python class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train
  • sklearn.neighbors.KDTree
    ``` class sklearn.neighbors.KDTree(X, leaf_size=40, metric='minkowski', **kwargs) ``` KDTree用于快
  • sklearn.feature_selection.SelectPercentile
    ```python class sklearn.feature_selection.SelectPercentile(score_func=, *, perc

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