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sklearn.datasets.load_breast_cancer
```python sklearn.datasets.load_breast_cancer(*, return_X_y=False, as_frame=False) ``` [[源码]](h
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sklearn.metrics.consensus_score
```python sklearn.metrics.consensus_score(a, b, *, similarity='jaccard') ``` [源码](https://githu
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sklearn.utils.gen_even_slices
```python sklearn.utils.gen_even_slices(n, n_packs, *, n_samples=None) ``` [源码](https://github.
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绘制L2正则化函数的岭系数图
岭回归是在这个例子中使用的估计器。左图中的每一种颜色表示系数向量的一个不同维度，表示为正则化参数的函数。右图表显示了解决方案的有多精确。此示例说明如何通过岭回归找到定义良好的解，以及正则化如何影响系数
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sklearn.linear_model.ElasticNet
```python class sklearn.linear_model.ElasticNet(alpha=1.0, *, l1_ratio=0.5, fit_intercept=True, norm
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sklearn.datasets.load_diabetes
```python sklearn.datasets.load_diabetes(*, return_X_y=False, as_frame=False) ``` [[源码]](https:
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sklearn.metrics.pairwise.additive_chi2_kernel
```python sklearn.metrics.pairwise.additive_chi2_kernel(X, Y=None) ``` [源码](https://github.com/
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sklearn.utils.graph.single_source_shortest_path_length
```Python sklearn.utils.graph.single_source_shortest_path_length(graph, source, *, cutoff=None) ``
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SGD: 惩罚
惩罚为L1、L2和弹性网的惩罚等于1的等高线。 所有这些都得到 [`SGDClassifier`](https://scikit-learn.org.cn/view/388.html) 和 [`SG
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sklearn.linear_model.ElasticNetCV
```python class sklearn.linear_model.ElasticNetCV(*, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=N
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sklearn.metrics.mean_absolute_error
```python sklearn.metrics.mean_absolute_error(y_true, y_pred, *, sample_weight=None, multioutput='u
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sklearn.datasets.load_digits
```python sklearn.datasets.load_digits(*, n_class=10, return_X_y=False, as_frame=False) ``` [[源
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sklearn.utils.sklearn.utils.graph_shortest_path.graph_shortest_path
```Python sklearn.utils.graph_shortest_path.graph_shortest_path() ``` 对正有向图或无向图执行最短路径图搜索。 |
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多项式插值
这个例子演示了如何用岭回归用n_degree多项式逼近一个函数。具体而言，从n_samples 1D点 出发，建立Vandermonde矩阵就足够了，它是n个样本 x n_degree+1，其形式如下
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sklearn.linear_model.Lars
```python class sklearn.linear_model.Lars(*, fit_intercept=True, verbose=False, normalize=True, prec
