How to balance classification using DecisionTreeClassifier?

Question

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 fo

Answer 1

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)