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混淆矩阵
本案例使用混淆矩阵评估鸢尾花数据集上分类器输出质量。对角线元素表示预测标签等于真实标签的点数，而非对角线元素则是分类器未正确标记的点。混淆矩阵的对角线值越高越好，表明正确的预测越多。 这些图按类
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非线性支持向量机
本案例是使用带有RBF核的非线性SVC执行二分类任务。预测目标是输入的XOR数据。颜色图说明了SVC学习的决策功能。 （译者注：XOR是Exclusive-OR gate，是异或门的简称，是又数
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sklearn.cluster.MeanShift
```python class sklearn.cluster.MeanShift(*, bandwidth=None, seeds=None, bin_seeding=False, min_bin
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sklearn.linear_model.MultiTaskLassoCV
```python class sklearn.linear_model.MultiTaskLassoCV(*, eps=0.001, n_alphas=100, alphas=None, fit_
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sklearn.metrics.pairwise.sigmoid_kernel
```python sklearn.metrics.pairwise.sigmoid_kernel(X, Y=None, gamma=None, coef0=1) ``` [源码](http
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sklearn.datasets.make_friedman2
```python sklearn.datasets.make_friedman2(n_samples=100, *, noise=0.0, random_state=None) ``` [[源码]
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sklearn.decomposition.IncrementalPCA
```python class sklearn.decomposition.IncrementalPCA(n_components=None, *, whiten=False, copy=True,
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sklearn.ensemble.BaggingRegressor
```python class sklearn.ensemble.BaggingRegressor(base_estimator=None, n_estimators=10, *, max_samp
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sklearn.model_selection.StratifiedKFold
```python class sklearn.model_selection.StratifiedKFold(n_splits=5, *, shuffle=False, random_state=N
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sklearn.neighbors.DistanceMetric
```python class sklearn.neighbors.DistanceMetric ``` **DistanceMetric类。** 此类为快速距离度量功能提供统一的接口。 可以通过
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sklearn.feature_selection.GenericUnivariateSelect
```python class sklearn.feature_selection.GenericUnivariateSelect(score_func=
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sklearn.preprocessing.PolynomialFeatures
```python class sklearn.preprocessing.PolynomialFeatures(degree=2, *, interaction_only=False, includ
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sklearn.utils.sparsefuncs.inplace_swap_row
```Python sklearn.utils.sparsefuncs.inplace_swap_row(X, m, n) ``` [源码](https://github.com/sciki
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sklearn.gaussian_process.GaussianProcessRegressor
```python class sklearn.gaussian_process.GaussianProcessRegressor(kernel=None, *, alpha=1e-10, optim
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基于K均值的颜色量化
对颐和园(中国)的图像执行像素级矢量量化(VQ)，将显示该图像所需的颜色数量从96,615种独特颜色减少到64种，同时保持整体外观质量。 在本例中，像素在3D空间中表示，K-均值用于查找64种颜
