scikit-learn（工程中用的相对较多的模型介绍）：1.14. Semi-Supervised

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于 2015-08-07 09:09:10 发布 · 5.4k 阅读

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#scikit-learn #工程应用 #Semi-Supervised #半监督学习 #机器学习

scikit-learn的semi_supervised模块包含LabelPropagation和LabelSpreading，利用未标记数据改进模型，适用于少量标记样本和大量未标记样本场景。LabelPropagation采用硬夹紧，而LabelSpreading通过损失函数对噪声更鲁棒。两种模型都可以使用核方法，RBF核可能导致运行时间较长，KNN核则更节省内存。示例展示了它们在Iris数据集和手写数字识别中的应用。

参考：http://scikit-learn.org/stable/modules/label_propagation.html

The semi-supervised estimators insklearn.semi_supervised are able to make use of this additional unlabeled data to better capture the shape of the underlying data distribution and generalize better to new samples. These algorithms can perform well when we have a very small amount of labeled points and a large amount of unlabeled points.

Unlabeled entries in y：It is important to assign an identifier to unlabeled points along with the labeled data when training the model with the fit method. The identifier that this implementation uses is the integer value