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• datasets.fetch_species_distributions
  ```python sklearn.datasets.fetch_species_distributions(*, data_home=None, download_if_missing=True)
• sklearn.metrics.silhouette_score
  ```python sklearn.metrics.silhouette_score(X, labels, *, metric='euclidean', sample_size=None, rand
• sklearn.utils.extmath.fast_logdet
  ```Python sklearn.utils.extmath.fast_logdet(A) ``` [源码](https://github.com/scikit-learn/scikit-
• SGD：最大间隔分离超平面
  使用使用SGD训练的线性支持向量机分类器绘制两类可分离数据集中的最大间隔分离超平面。  ```
• sklearn.linear_model.Ridge
  ```Python class sklearn.linear_model.Ridge(alpha=1.0, *, fit_intercept=True, normalize=False, copy_X
• sklearn.datasets.get_data_home
  ```python sklearn.datasets.get_data_home(data_home=None) ``` [[源码]](https://github.com/scikit-l
• sklearn.metrics.silhouette_samples
  ```python sklearn.metrics.silhouette_samples(X, labels, *, metric='euclidean', **kwds) ``` [源码]
• sklearn.utils.extmath.density
  ```Python sklearn.utils.extmath.density(w, **kwargs) ``` [源码](https://github.com/scikit-learn/s
• SGD：凸损失函数
  比较[`sklearn.linear_model.SGDClassifier`](https://scikit-learn.org.cn/view/388.html)支持的各种凸损失函数的图。 ![
• sklearn.linear_model.SGDRegressor
  ```python class sklearn.linear_model.SGDRegressor(loss='squared_loss', *, penalty='l2', alpha=0.0001
• sklearn.datasets.load_boston
  ```python sklearn.datasets.load_boston(*, return_X_y=False) ``` [[源码]](https://github.com/sciki
• sklearn.metrics.v_measure_score
  ```python sklearn.metrics.v_measure_score(labels_true, labels_pred, *, beta=1.0) ``` [源码](https
• sklearn.utils.extmath.weighted_mode
  ```python sklearn.utils.extmath.weighted_mode(a, w, *, axis=0) ``` [源码](https://github.com/sci
• 普通最小二乘与岭回归差异
  由于每个维度中的几个点以及线性回归所使用的直线尽可能地跟随这些点，观测数据上的噪声将导致第一幅图中所示的巨大差异。由于观测中产生的噪声，每条线的斜率对每个预测都会有很大的影响。 岭回归基本上是最
• sklearn.linear_model.RidgeCV
  ```python class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), *, fit_intercept=True, normali

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