Description: The Balanced Random Forest is an ensemble learning method that combines multiple decision trees, optimizing their performance by addressing the class imbalance problem. This approach is based on the idea that combining several models can enhance the accuracy and robustness of predictions. In a Balanced Random Forest, multiple decision trees are generated from random subsets of data, and each tree is trained independently. However, unlike a traditional random forest, this method implements balancing techniques, such as undersampling or oversampling, to ensure that minority classes are adequately represented in the training set. This is crucial in situations where data is imbalanced, as models may tend to favor the majority class, resulting in poor performance in predicting the minority class. Ultimately, the predictions from all trees are combined, typically through voting, to produce a final prediction that is more accurate and reliable. This approach not only improves accuracy but also increases the model’s generalization, making it more effective in various applications.
