scikit-learn中的决策功能是否可以返回到超平面的真实距离? [英] Does the decision function in scikit-learn return the true distance to the hyperplane?

问题描述

决策函数是否返回每个样本到超平面的实际距离此处.还是您需要此处.应该使用哪种方法?

Does the decision function return the actual distance to the hyperplane for each sample as stated here. Or do you have to the extra calculation as shown here. Which method should be used?

推荐答案

否，这不是实际距离.并视情况而定，您(线性内核)或可能(非线性内核)无法将其转换为实际距离.在这里是另一个很好的解释.没关系，是的，您必须采取那一步(如果您想要实际距离).

No, that's not the actual distance. And depends on the case, you may (linear kernel) or may not (non-linear kernel) be able to convert that to an actually distance. Here is another good explanation. Not matter what, yes you have to take that extra step if you want the actual distance.

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