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Lasso和弹性网
LASSO和弹性网(L1和L2惩罚)使用坐标下降来实现。 可以强迫系数为正。 
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显示对象的可视化
在此示例中,我们将直接从ConfusionMatrixDisplay,RocCurveDisplay和PrecisionRecallDisplay各自的指标中构造显示对象。当模型的预测已经计算或计算成
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嵌套与非嵌套交叉验证
本案例在鸢尾花数据集的分类器上比较了非嵌套和嵌套的交叉验证策略。嵌套交叉验证(CV)通常用于训练还需要优化超参数的模型。嵌套交叉验证估计基础模型及其(超)参数搜索的泛化误差。选择最大化非嵌套交叉验证结
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缓存最近邻
此案例演示了如何在KNeighborsClassifier类使用k个最近邻点之前对最近邻点进行预计算。 KNeighborsClassifier可以在内部计算最近邻,但是预计算它们可以带来很多好处,例
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支持向量机:核函数
下面显示了三种不同类型的SVM核函数。当数据点不可线性分离时,多项式和RBF特别有用。  ![](
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sklearn.feature_selection.SelectorMixin
```python class sklearn.feature_selection.SelectorMixin ``` [[源码]](https://github.com/scikit-le
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sklearn.cluster.cluster_optics_xi
```python sklearn.cluster.cluster_optics_xi(*, reachability, predecessor, ordering, min_samples, mi
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sklearn.covariance.MinCovDet
```python class sklearn.covariance.MinCovDet(*, store_precision=True, assume_centered=False, support
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sklearn.linear_model.PassiveAggressiveRegressor
```python sklearn.linear_model.PassiveAggressiveRegressor(*, C=1.0, fit_intercept=True, max_iter=10
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sklearn.manifold.LocallyLinearEmbedding
```python class sklearn.manifold.LocallyLinearEmbedding(*, n_neighbors=5, n_components=2, reg=0.001,
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sklearn.metrics.pairwise_distances_argmin_min
```python sklearn.metrics.pairwise_distances_argmin_min(X, Y, *, axis=1, metric='euclidean', metric
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sklearn.datasets.make_regression
```python sklearn.datasets.make_regression(n_samples=100, n_features=100, *, n_informative=10, n_ta
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sklearn.decomposition.TruncatedSVD
```python class sklearn.decomposition.TruncatedSVD(n_components=2, *, algorithm='randomized', n_iter
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sklearn.ensemble.RandomForestRegressor
```python class sklearn.ensemble.RandomForestRegressor(n_estimators=100, *, criterion='mse', max_dep
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sklearn.model_selection.ParameterSampler
```python class sklearn.model_selection.ParameterSampler(param_distributions, n_iter, *, random_sta