PERFORMANCE SPEED AND ACCURACY METRICS OF FEATURE CLASSIFICATION MODELS Shukrulloev Bektosh

Аннотация

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. Keywords: Feature classification, accuracy metrics, inference speed, machine learning, model performance, F1-score, real-time classification. Introduction. Feature classification models are at the core of decision-making systems powered by artificial intelligence and machine learning. They enable machines to distinguish between categories — such as identifying whether an email is spam, recognizing human faces, or detecting medical anomalies in radiographic images. However, as these models become more deeply embedded in high-stakes domains like autonomous driving or diagnostics, a critical challenge arises: achieving high classification accuracy without compromising performance speed. While accuracy ensures reliable predictions, speed — both in training and inference — determines real-time applicability. For instance, a highly accurate model that takes several seconds to infer results is impractical for real-time video surveillance. Conversely, a faster model might lack the nuanced understanding required in complex visual contexts. Thus, evaluating models across both dimensions is essential for selecting optimal algorithms under specific conditions. This study expands the understanding of classification models by comparing their real- world accuracy metrics and speed parameters on standardized datasets using consistent benchmarking tools. Methods and Evaluation Criteria Datasets. The datasets were chosen to represent varying levels of complexity: • MNIST offers grayscale digit images (28x28), ideal for benchmarking lightweight classifiers. • CIFAR-10 presents more complex, colored images (32x32) across 10 classes, testing image processing depth. 67 “YANGI O‘ZBEKISTONDA ZAMONAVIY PSIXOLOGIYA VA PEDAGOGIKAGA DOIR MUAMMOLARNI TADQIQ ETISHNING TRANSFORMATSION IMKONIYATLARI” Xalqaro ilmiy - amaliy konferensiyasi, 2025-yil 24-aprel • UCI Iris Dataset, while small, is ideal for quick prototyping and baseline metric comparison. Models Analyzed. A range of both traditional and deep learning models was selected to evaluate the spectrum of complexity and performance: • Naïve Bayes (NB) for statistical simplicity. • K-NN, known for its non-parametric nature and clarity. • SVM, well-regarded for handling nonlinear separable data. • Decision Tree, due to its interpretability. • CNN, representing modern deep learning models with state-of-the-art accuracy. Performance Metrics. Evaluation covered: • Training Time: Time to fully train the model. • Inference Time: Delay introduced during prediction on a single instance. • Memory Usage: RAM consumed during inference. • Accuracy, Precision, Recall, and F1-score: Derived from the confusion matrix, capturing both correctness and robustness of classification. Each model was executed under identical computational conditions (Intel Core i7, 16GB RAM, NVIDIA GTX 1650) to ensure fair comparison. Results. The results, visualized in the provided table, show marked differences: • CNN dominated accuracy with 97.8% and F1-score of 0.96, confirming its superiority in handling image data with complex patterns. However, it demanded the highest training time (120.3 s) and RAM (780 MB), limiting its use in embedded systems. • SVM achieved a strong balance, with 91.5% accuracy, 0.90 F1-score, and moderate training/inference costs, making it ideal for structured but high-dimensional data. • K-NN, while accurate (86.7%), suffered from slower inference (1.5 ms) due to runtime distance calculations — making it unsuitable for low-latency applications. • Naïve Bayes offered minimal computational overhead but compromised on precision. Nonetheless, its sub-second training time (0.5 s) makes it ideal for fast-deploy scenarios like spam filters. • Decision Trees provided a solid middle ground — interpretable, quick, and reasonably accurate. These differences demonstrate the importance of choosing a model not just for accuracy, but for how and where it will be deployed. Discussion. The trade-off between speed and accuracy is highly dependent on the intended application: • In mobile health diagnostics, lightweight models like Decision Trees or Naïve Bayes are preferred due to limited processing power. • For autonomous navigation, latency must be extremely low, pushing the nee