问题
I have a data set where the classes are unbalanced. The classes are either 0, 1 or 2.
How can I calculate the prediction error for each class and then re-balance weights accordingly in Sklearn.
回答1:
If you want to fully balance (treat each class as equally important) you can simply pass class_weight='balanced', as it is stated in the docs:
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
回答2:
If the frequency of class A is 10% and the frequency of class B is 90%, then the class B will become the dominant class and your decision tree will become biased toward the classes that are dominant
In this case, you can pass a dic {A:9,B:1} to the model to specify the weight of each class, like
clf = tree.DecisionTreeClassifier(class_weight={A:9,B:1})
The class_weight='balanced' will also work, It just automatically adjusts weights according to the proportion of each class frequencies
After I use class_weight='balanced', the record number of each class has become the same (around 88923)
来源：https://stackoverflow.com/questions/37522191/sklearn-how-to-balance-classification-using-decisiontreeclassifier