带有huber损失的python svm函数 [英] python svm function with huber loss

问题描述

我需要具有Huber损失功能的python的svm分类器。但是它的默认损失功能是铰链损失。您知道如何为Python svm分配损失函数吗？

  svc = svm.SVC（kernel ='linear'，C = 1，gamma = 1）。fit（数据，标签）
  

解决方案

实际上，没有带有Huber损失的SVM之类的东西，因为SVM实际上是经过铰链损失训练的线性（或内核化）模型。如果您更改损失-它不再是SVM。因此，库没有损失参数，因为更改它不适用于SVM概念。

如果您想使用模型您可以使用 SGDClassiifier

如果您想做更复杂的事情，例如带有这种惩罚-sklearn不是一个好选择，您应该查看更多的低级库，例如TF，Keras等。

I need a svm classifier of python with huber loss function. But its default loss function is hinge loss. Do you know how can I assign loss function to python svm?

svc = svm.SVC(kernel='linear', C=1, gamma=1).fit(data, label)

解决方案

There is really no such thing as "SVM with huber loss", as SVM is literally a linear (or kernelized) model trained with hinge loss. If you change the loss - it stops being SVM. Consequently libraries do not have a loss parameter, as changing it does not apply to the SVM concept.

If you want to train a model with huber loss you can use SGDClassiifier from sklearn, which will train a linear model with this (and many other) loss.

If you want to do something more complex, like non-linear model with this kind of penalty - then sklearn is not the good choice and you should look at more "low-level" libraries such us TF, Keras and so on.

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