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

Introduction

Data mining uses techniques from statistics, machine learning, AI, and databases to extract useful patterns from large datasets, aiding decision-making and prediction. Machine Learning allows systems to learn from data without explicit programming.

Random Forest is an ensemble algorithm that combines multiple decision trees to improve accuracy and reduce overfitting, useful for classification and regression.

Random Forest Regression predicts continuous values, and when combined with Bayesian probability, it enhances feature selection and classification, improving prediction accuracy and robustness.

The Bayesian-based Random Forest algorithm preprocesses data, trains models for different price categories, applies Bayes’ theorem for probability calculations, and predicts outcomes via weighted averages. This method offers higher accuracy, better handling of categories, and less bias.

Applications include real estate pricing, stock forecasting, customer behavior analysis, and fraud detection.

Conclusion

Random Forest Regression improves prediction accuracy by averaging the outcomes of multiple decision trees, reducing variance and overfitting. It relies on data-driven feature selection methods like Mean Decrease in Impurity and Permutation Importance. Bayesian Probability-Based Random Forest Regression enhances this by incorporating prior knowledge and conditional probabilities, making the model more adaptive and accurate. Bayesian methods assign significance to each feature, refining predictions and improving interpretability. While Bayesian approaches increase computational complexity, they provide better adaptability in dynamic environments. The combination of statistical learning and probabilistic reasoning enhances overall model performance and predictive reliability.

References

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Copyright

Copyright © 2025 K. Usha Rani, Dr. K. Venkataramana . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.