2026· International Journal of Latest Technology in Engineering, Management & Applied Science· Vol 15, pp. 872-881· 0 citations
TL;DR
This study presents a comprehensive comparative evaluation of three widely used machine learning algorithms: Logistic Regression, Decision Tree, and Random Forest, for classification tasks, and demonstrates that Random Forest achieves superior generalization performance compared to the other models.
Abstract
Artificial Intelligence (AI) has significantly transformed data analytics by enabling intelligent and data-driven decision-making through advanced computational models. This study presents a comprehensive comparative evaluation of three widely used machine learning algorithms: Logistic Regression, Decision Tree, and Random Forest, for classification tasks. The analysis is performed on a structured dataset incorporating systematic preprocessing, feature engineering, and model optimization techniques. Model performance is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The experimental results demonstrate that Random Forest achieves superior generalization performance compared to the other models. The findings emphasize the critical role of selecting appropriate models based on dataset characteristics and provide valuable insights for future research in predictive analytics.
The growing importance of data-driven decision making in healthcare, and also other domains like intelligent analytics, has motivated the need for models that are able to achieve high predictive performance while still being interpretable. However, prior works often focused on data mining that contribute transparent ru...
Kanakam Sadhikumar· Advanced International Journ...· 0 citations
This study comparatively evaluated the predictive performance of selected machine-learning classification algorithms for high-dimensional data using a quantitative computational design-and-evaluation methodology. The analysis involved data preprocessing, feature processing, model development, hyperparameter optimizatio...
Muhammad Awais, Muhammad Haad, Hameed Hussain et al.· Journal of Global Social Tra...· 0 citations
Logistic Regression, Random Forest, soft voting, and weighted voting using a publicly available educational dataset comprising 4,424 student records, 36 predictors, and three outcome classes provided accurate, interpretable, and decision-oriented predictions, although external validation and prospective evaluation are...
Haleeful Jud· Journal of Intelligent Decis...· 0 citations
Overall, logistic regression demonstrates strong and good generalization ability, while ensemble methods constitute robust alternatives for binary classification on structured data.
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This study examined advanced machine learning approaches for predictive analytics by comparatively evaluating the classification performance of Support Vector Machine (SVM), Random Forest, and Gradient Boosting in high-dimensional data. A high-dimensional dataset containing multiple observations and a large number of f...
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