Our classification problem is often unbalanced, with some classes having much more samples than other.
If we are interested in evaluating the problem in a way that each class gets equal weight (macro average) we could consider training the model using weights per class. In sklearn, this is achieved by passing an array sample_weight when calling
model.fit(X, y, sample_weight)
where the sample_weight that gives more weight to under-represented classes can be easily computed using sklearn's compute_sample_weight.
Our classification problem is often unbalanced, with some classes having much more samples than other.
If we are interested in evaluating the problem in a way that each class gets equal weight (
macroaverage) we could consider training the model using weights per class. Insklearn, this is achieved by passing an arraysample_weightwhen callingwhere the
sample_weightthat gives more weight to under-represented classes can be easily computed usingsklearn'scompute_sample_weight.