APPLYING DECISION TREE MODELS TO SOLVE REAL-LIFE PROBLEMS

Abstract

This article delves into the practical application of decision tree models for solving real-world challenges. It investigates a range of algorithms, including CART, ID3, Regression, C4.5, Random Forest, Hist Gradient Boosting, Gradient Boosting, and Adaboost. The mathematical underpinnings of these models are elucidated, and a versatile framework is employed to evaluate their performance across diverse datasets. The primary objective is to showcase the efficacy of decision tree models in addressing real-life problems spanning various domains. Through performance analyses, the article sheds light on algorithm strengths and limitations, aiding practitioners in selecting the most suitable approach for specific problem contexts.