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April 3, 2026Open Access

Comparative analysis of machine learning classifiers

Key Points

To compare the effectiveness of five machine learning classification algorithms across different datasets.
Compared performance of SVM, MLP, CART, K-NN, and NB classifiers.
Utilized nested cross-validation for performance evaluation.
Applied algorithms to Heart Disease, German Credit, Spambase, and Online Shoppers Purchasing Intention datasets.
No single classifier consistently outperformed others across all datasets.
Dataset characteristics significantly influenced classifier performance.
Class imbalance was identified as a major issue.
Simpler algorithms showed competitive performance with lower computational costs.

Abstract

This study presents a comparative analysis of five machine learning classification algorithms: support vector machine (SVM), multilayer perceptron (MLP), classification and regression tree (CART), k-nearest neighbors algorithm (K-NN), and naive Bayes classifier (NB) across four datasets from various domains. Using nested cross-validation, the research evaluated classifier performance on Heart Disease, German Credit, Spambase, and Online Shoppers Purchasing Intention datasets. Results demonstrated that no single classifier consistently outperformed others across all datasets and selection should be based on dataset characteristics and application requirements. Dataset characteristics emerged as the primary factor influencing performance, with class imbalance proving particularly problematic. Training efficiency analysis revealed that simpler algorithms can maintain competitive performance with lower computational costs.

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