Optimization of the Random Forest Algorithm

Abstract

Optimization algorithms are implemented for making the field of machine learning more efficient by comparing various solutions until an optimum or a satisfactory answer is found to yield a better accuracy score than the earlier existing one. In this paper, optimization of the Random Forest is performed which is a supervised learning model for classification and regression. A detailed analysis of the optimization technique of this model is done, which follows the unequal weight voting strategy, where weight is assigned based on how well an individual tree performs.