scikit-learn的k均值:预测方法实际上有什么作用? [英] scikit-learn's k-means: what does the predict method really do?

问题描述

当我使用scikit-learn的k-means实现时，我通常只调用fit()方法，这足以获取聚类中心和标签. predict()方法用于计算标签，甚至为方便起见也可以使用fit_predict()方法，但是如果我只能使用fit()获得标签，那么predict()方法的目的是什么?

When I use scikit-learn's implementation of k-means I usually just call the fit() method and that is enough to get the cluster centers and the labels. The predict() method is used to calculate the labels and even a fit_predict() method is available for convenience, but if I can get the labels only using fit(), what is the purpose of the predict() method?

推荐答案

predict可以用于看不见的数据.当k均值用于半监督学习中的特征提取时，此方法(以及transform方法更有用):将大量样本聚类，然后将最接近的质心/距离作为质心作为后续监督学习的特征问题.将结果用于预测时，您会得到k均值未看到的样本.

predict, as @EdChum suggested, can be used on unseen data. This (and more so, the transform method) is useful when k-means is used for feature extraction in semisupervised learning: you cluster a large set of samples, then use nearest centroid/distance to centroids as features for a subsequent supervised learning problem. When using the result for prediction, you get samples that were not seen by k-means.

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