Overfitting in Machine Learning:A Comparative Analysis of Decision Trees and Random Forests  

作　　者：Erblin Halabaku Eliot Bytyçi 

机构地区：[1]Department of Mathematics,Faculty of Mathematical and Natural Sciences,University of Prishtina,Prishtina,10000,Kosovo

出　　处：《Intelligent Automation & Soft Computing》2024年第6期987-1006,共20页智能自动化与软计算（英文）

摘　　要：Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data.This paper presents a comprehensive analysis of machine learning algorithms,focusing on the structure and efficacy of random forests in mitigating overfitting—a prevalent issue in decision tree models.It also introduces a novel approach to enhancing decision tree performance through an optimized pruning method called Adaptive Cross-Validated Alpha CCP(ACV-CCP).This method refines traditional cost complexity pruning by streamlining the selection of the alpha parameter,leveraging cross-validation within the pruning process to achieve a reliable,computationally efficient alpha selection that generalizes well to unseen data.By enhancing computational efficiency and balancing model complexity,ACV-CCP allows decision trees to maintain predictive accuracy while minimizing overfitting,effectively narrowing the performance gap between decision trees and random forests.Our findings illustrate how ACV-CCP contributes to the robustness and applicability of decision trees,providing a valuable perspective on achieving computationally efficient and generalized machine learning models.

关 键 词：Artificial intelligence decision tree random forest PRUNE OVERFITTING 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]