Exploring the Application of Random Forest Methods in Statistical Classification and Regression Problems

Posted: Feb 08, 2023

• Anthony Parker
• Ariana Cooper
• Ashley Diaz

Abstract

The field of statistical learning has witnessed remarkable advancements in ensemble methods, with Random Forest algorithms emerging as particularly powerful tools for both classification and regression tasks. Originally introduced by Leo Breiman, Random Forest methods combine multiple decision trees to create robust predictive models that mitigate the overfitting tendencies of individual trees. While these methods have demonstrated considerable success across various domains, their application often follows conventional patterns that fail to leverage the full potential of ensemble learning principles. This research seeks to transcend traditional boundaries by exploring novel methodological extensions and unconventional applications of Random Forest techniques. Our investigation is motivated by several critical observations regarding current limitations in Random Forest implementations. First, the static nature of traditional feature selection processes often overlooks complex interdependencies within high-dimensional datasets. Second, the uniform tree-depth parameters commonly employed fail to account for the heterogeneous complexity of different regions within the feature space. Third, the interpretability of Random Forest models remains challenging, particularly when applied to emerging problem domains where domain knowledge is limited. These limitations present opportunities for methodological innovation that can enhance both the performance and applicability of Random Forest methods.