Regression for Imbalanced Data with Application

Description

Summary

• Introduction and motivation Imbalanced data are the situation where the less represented observations of the data are of the main interest.
• Gaussian noise and SMOGN algorithms are a mixture of both under/oversampling techniques.
• Second, define the relevance and utility functions to evaluate the performance of the model.
• In contrast to the MSE and RMSE, we look for the maximum value (close to 1) for the recall, precision, and F1 score to reflect better performance.