Applying Minority Range to Gini Index to Handle Imbalanced Dataset in Decision Tree Classifiers

Abstract

The Class imbalance Problem is a common problem in Machine Learning where the number of instances in one class is significantly lower than the other, this can lead to biased classification models where the majority class dominates and the minority class is mis- misclassified. Decision Tree Classifiers are commonly used for classification tasks due to their simplicity and in interpretability. However, the class imbalance Problem can negatively impact the performance of decision tree Classifiers [1]. In this paper, we discuss a new approach to training a decision tree classifier that is an improvement over a pre-existing approach. We also provide experiments to prove that the proposed method is an improvement over the preexisting metrics