问题描述
我正在处理类别不平衡(5％1)的分类问题.我想预测班级，而不是概率.
I'm working on a classification problem with unbalanced classes (5% 1's). I want to predict the class, not the probability.
在二进制分类问题中，默认情况下scikit的classifier.predict()是否使用0.5?如果不是，默认方法是什么?如果可以，该如何更改?
In a binary classification problem, is scikit's classifier.predict() using 0.5 by default?If it doesn't, what's the default method? If it does, how do I change it?
在scikit中，某些分类器具有class_weight='auto'选项，但并非全部都具有.使用class_weight='auto'，.predict()会使用实际人口比例作为阈值吗?
In scikit some classifiers have the class_weight='auto' option, but not all do. With class_weight='auto', would .predict() use the actual population proportion as a threshold?
在不支持class_weight的分类器(如MultinomialNB)中，如何做到这一点?除了使用predict_proba()然后自己计算类之外.
What would be the way to do this in a classifier like MultinomialNB that doesn't support class_weight? Other than using predict_proba() and then calculation the classes myself.
推荐答案
在概率分类器中，是的.正如其他人所解释的那样，从数学角度来看，这是唯一明智的阈值.
In probabilistic classifiers, yes. It's the only sensible threshold from a mathematical viewpoint, as others have explained.
您可以设置class_prior，这是每个类别 y 的先验概率P( y ).这有效地改变了决策边界.例如
You can set the class_prior, which is the prior probability P(y) per class y. That effectively shifts the decision boundary. E.g.
# minimal dataset
>>> X = [[1, 0], [1, 0], [0, 1]]
>>> y = [0, 0, 1]
# use empirical prior, learned from y
>>> MultinomialNB().fit(X,y).predict([1,1])
array([0])
# use custom prior to make 1 more likely
>>> MultinomialNB(class_prior=[.1, .9]).fit(X,y).predict([1,1])
array([1])
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