Improved Random Forest Regression for Prediction

Abstract: We propose an improved Random Forest Regression model that selects features based on probability-driven risk factors instead of random selection. Using Bayes’ Theorem, we assign selection probabilities based on high, medium, and low risk levels, ensuring optimal feature importance. The model iteratively refines feature selection and limits tree growth based on a theoretical upper bound. A weighted averaging mechanism enhances prediction accuracy by adjusting tree contributions based on probabilistic relevance. Experimental results show improved prediction accuracy with reduced complexity, outperforming conventional regression models in various datasets