PERFORMANCE SPEED AND ACCURACY METRICS OF FEATURE CLASSIFICATION MODELS

The efficiency of feature classification models in machine learning and artificial intelligence is primarily evaluated through two essential dimensions: performance speed and classification accuracy. This paper investigates the trade-off between these two aspects across different classification algorithms, including K-NN, SVM, Naïve Bayes, Decision Trees, and Deep Neural Networks. Through empirical evaluation on benchmark datasets (MNIST, CIFAR-10, UCI), we analyze training time, inference time, memory consumption, and accuracy-related metrics such as precision, recall, and F1-score. The results provide insight into selecting optimal models based on application-specific constraints such as real-time requirements or accuracy sensitivity.