Nu是不可行的 [英] Nu is infeasible

问题描述

我在sklearn中使用NuSVC类.尝试实例化NuSVC对象后，如下所示:

I am using the NuSVC class in sklearn. After trying to instantiate an NuSVC object as follows:

self.classifier = OneVsRestClassifier(NuSVC())

我发现我反复收到指定的nu是不可行的"错误.我尝试将'nu'参数从0.1一直更改为1(以0.1为增量)，但是我仍然遇到相同的错误.我真的不确定如何解释此消息，以及如何解决它?我认为，如果将nu设置为1，那将是可行的，因为nu代表了训练误差部分的上限，我认为该误差应该始终成立.可能是什么原因造成的?

I found that I repeatedly get a 'specified nu is infeasible' error. I tried varying the 'nu' parameter from 0.1 all the way to 1. (in 0.1 increments), but I keep getting the same error. I am really unsure how to interpret this message, and how to go about resolving it? I figured that if I set the nu to 1., it would work because nu represents an upper bound on the fraction of training errors which I believe should always be tenable. What could be causing this?

感谢您的帮助！

推荐答案

为完整起见，从文档中获得:Nu-SVM是SVM的一种受约束的表述(等同于原始的直到重新参数化)，对允许的错误分类提出了严格的要求.如果无法满足此界限，则相关的凸优化问题将变得不可行.

For completeness, from the documentation: Nu-SVM is a constrained formulation of SVM (equivalent with the original up to reparametrization) which poses a hard bound on the allowed misclassification. If this bound cannot by satisfied, then the associated convex optimization problem becomes infeasible.

从这个角度出发，您需要调查的第一件事是您真正可以预期到多少训练错误，并可能会修改您的假设.在C值的网格中搜索标准SVM进行检查.

From this standpoint the first thing you have to investigate is how much training error you really can expect, and maybe revise your assumptions. Search over a grid of C values for a standard SVM to check that.

NuSVC应该使用一些严格小于1的值.根据您的描述，您尝试过0.9-开始添加9，即.99，.999.如果在某些时候不起作用，那么某处必须存在另一个问题.

NuSVC should work with some values strictly less than 1, though. According to your description, you have tried 0.9 -- start adding 9s, ie .99, .999. If it doesn't work at some point, then there has to be another problem somewhere.

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