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  • sklearn.datasets.make_gaussian_quantiles
    ```python sklearn.datasets.make_gaussian_quantiles(*, mean=None, cov=1.0, n_samples=100, n_features
  • sklearn.decomposition.MiniBatchDictionaryLearning
    ```python class sklearn.decomposition.MiniBatchDictionaryLearning(n_components=None, *, alpha=1, n_
  • sklearn.ensemble.ExtraTreesRegressor
    ```python class sklearn.ensemble.ExtraTreesRegressor(n_estimators=100, *, criterion='mse', max_depth
  • sklearn.model_selection.TimeSeriesSplit
    ```python class sklearn.model_selection.TimeSeriesSplit(n_splits=5, *, max_train_size=None) ```
  • sklearn.neighbors.KernelDensity
    ``` lass sklearn.neighbors.KernelDensity(*, bandwidth=1.0, algorithm='auto', kernel='gaussian', metr
  • sklearn.feature_selection.SelectKBest
    ```python class sklearn.feature_selection.SelectKBest(score_func=, *, k=10) ``
  • sklearn.preprocessing.QuantileTransformer
    ```python class sklearn.preprocessing.QuantileTransformer(*, n_quantiles=1000, output_distribution='
  • sklearn.utils.sparsefuncs.mean_variance_axis
    ```Python sklearn.utils.sparsefuncs.mean_variance_axis(X, axis) ``` [源码](https://github.com/sci
  • sklearn.gaussian_process.ConstantKernel
    ```python class sklearn.gaussian_process.kernels.ConstantKernel(constant_value=1.0, constant_value_b
  • 不同度量的聚集聚类
    演示不同度量对分层聚类的影响。 该示例的设计表明了选择不同度量标准的效果。它应用于波形，可视为高维向量。实际上，度量之间的差异通常在高维(特别是欧几里得度量和城市街道度量)中更为明显。 我
  • 1.6 最近邻
    [`sklearn.neighbors`](http://scikit-learn.jg.com.cn/lists/3.html#sklearn.neighbors%EF%BC%9A%E6%9C%80
  • Beta-divergence损失函数
    一种比较各种Beta-divergence损失函数的图，它由 [`sklearn.decomposition.NMF`](https://scikit-learn.org.cn/view/609.ht
  • 梯度提升回归
    此示例演示了梯度提升从一组弱预测模型生成预测模型。梯度提升可以用于回归和分类问题。在这里，我们将训练一个模型来处理糖尿病回归任务。我们将从梯度提升回归器中得到结果， 这个梯度提升回归器是使用最小二乘损
  • Theil-Sen回归
    在合成数据集上计算Theil-Sen回归。 有关回归器的更多信息，请参见 [Theil-Sen estimator: generalized-median-based estimator](http
  • 使用带有交叉验证的网格搜索进行参数估计
    此案例显示了如何通过交叉验证来优化分类器。交叉验证是通过在开发集上（development set）使用sklearn.model_selection.GridSearchCV对象完成的，开发集是已有

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