Dummy Estimators and Handling Imbalance Class Problem

Dummy Estimators

Dummy Estimators help us to define a baseline model on the problem at hand. We saw them in case of Regression problems too. In the case of Classification, we have the following Dummy Estimators.

• stratified: It predicts the random class label by respecting the training set class distribution.

• most_frequent: It always predicts the most common label in the training dataset.

• prior: It predicts the class which maximizes the class prior.

• uniform: It generates the predictions uniformly at random.

• constant: It always predicts the constant label provided by the user.

prior always predicts the class that maximizes the class prior (like most_frequent) and predict_proba returns the class prior.