Optimizing Machine Learning Algorithms to Accelerate Smoking Detection

Abstract

This study evaluates the performance of various classification algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (Gboost), on a binary classification task. The results reveal that CNN achieves perfect performance, with an accuracy of 1.00 and precision, recall, and F1-scores of 1.00 for both classes. Similarly, SVM, Decision Tree, KNN, and Gboost also demonstrate flawless performance across all metrics. In contrast, GNB underperforms significantly, with an accuracy of 0.78 and lower precision, recall, and F1-scores, particularly for the "no" class. These findings highlight CNN's robustness and reliability, positioning it as a top-performing algorithm for this classification task. The study underscores the effectiveness of CNN and other high-performing algorithms while identifying limitations in GNB. Future research could focus on optimizing computational efficiency and scalability for real-world applications.