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Search finished, found 840 page(s) matching the search query.
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sklearn.linear_model.MultiTaskLasso
```python class sklearn.linear_model.MultiTaskLasso(alpha=1.0, *, fit_intercept=True, normalize=Fal
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sklearn.metrics.pairwise.rbf_kernel
```python sklearn.metrics.pairwise.rbf_kernel(X, Y=None, gamma=None) ``` [源码](https://github.co
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sklearn.datasets.make_friedman1
```python sklearn.datasets.make_friedman1(n_samples=100, n_features=10, *, noise=0.0, random_state=N
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sklearn.decomposition.FastICA
```python class sklearn.decomposition.FastICA(n_components=None, *, algorithm='parallel', whiten=Tr
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sklearn.ensemble.BaggingClassifier
```python class sklearn.ensemble.BaggingClassifier(base_estimator=None, n_estimators=10, *, max_sam
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sklearn.model_selection.ShuffleSplit
```python class sklearn.model_selection.ShuffleSplit(n_splits=10, *, test_size=None, train_size=Non
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sklearn.neighbors.BallTree
```python class sklearn.neighbors.BallTree(X, leaf_size=40, metric='minkowski', **kwargs) ``` BallT
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sklearn.preprocessing.OrdinalEncoder
```python class sklearn.preprocessing.OrdinalEncoder(*, categories='auto', dtype=
- sklearn.utils.sparsefuncs.inplace_row_scale
```Python sklearn.utils.sparsefuncs.inplace_row_scale(X, scale) ``` [源码](https://github.com/sci- sklearn.gaussian_process.GaussianProcessClassifier
```python class sklearn.gaussian_process.GaussianProcessClassifier(kernel=None, *, optimizer='fmin_l- DBSCAN聚类算法的演示
发现高密度的核心样本并从中膨胀聚类。  ```python Estimated numb- sklearn.base.BaseEstimator
```python class sklearn.base.BaseEstimator ``` [[源码]](https://github.com/scikit-learn/scikit-le- 1.3 内核岭回归
内核岭回归(Kernel ridge regression (KRR)) [M2012] , 是组合使用了内核技巧和岭回归(进行了l2正则化的最小二乘法)。因此，它所学习到的在空间中不同的线性函数是由- 梯度提升回归的预测区间
此示例演示如何使用分位数回归来创建预测区间。  ```python import num- 正交匹配追踪
用正交匹配追踪法从带噪测量信号中提取稀疏信号。  ```python print(__doc - sklearn.utils.sparsefuncs.inplace_row_scale
