ValueError:惩罚期限必须为正 [英] ValueError: Penalty term must be positive

问题描述

当我使用逻辑回归拟合模型时，向我显示了诸如ValueError的值错误:惩罚项必须为正.

When I'm fit my model using logistic regression showing me a value error like ValueError: Penalty term must be positive.

C=[1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3, 1e4]
for i in C[-9:]:
    logisticl2 = LogisticRegression(penalty='l2',C=C)
    logisticl2.fit(X_train,Y_train)
    probs = logisticl2.predict_proba(X_test)

得到错误:

ValueError:惩罚期限必须为正；得到了(C = [0.0001，0.001，0.01、0.1、1.0、10.0、100.0、1000.0、10000.0]]

ValueError: Penalty term must be positive; got (C=[0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0, 10000.0])

推荐答案

更仔细地看，您会发现您正在运行一个循环，其中代码没有任何变化-它始终是 C = C ，与您的 i 的当前值无关.而且您会得到预期的错误，因为 C 必须是浮点数，而不是列表(

Looking more closely, you'll realize that you are running a loop in which nothing changes in your code - it is always C=C, irrespectively of the current value of your i. And you get an expected error, since C must be a float, and not a list (docs).

如果我怀疑您要为 C 列表中的所有值运行逻辑回归分类器，则应按照以下方法修改代码:

If, as I suspect, you are trying to run your logistic regression classifier for all the values in your C list, here is how you should modify your code:

C=[1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3, 1e4]
for i in C:                                             # 1st change
    logisticl2 = LogisticRegression(penalty='l2',C=i)   # 2nd change
    logisticl2.fit(X_train,Y_train)
    probs = logisticl2.predict_proba(X_test)

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