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
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))