Dummy Estimators and Handling Imbalance Class Problem

You will learn about Dummy Estimators and handling imbalance class problems in this lesson. Dummy estimators help develop baseline models for classification. The Imbalanced class problem is a common problem, and there are several techniques to deal with it.

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.

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