An Integrated Framework for Comprehensive Evaluation and Visualization of Machine Learning Model Performance

This research presents a comprehensive evaluation framework for machine learning models that integrates multiple performance assessment metrics and visualization techniques to ensure accuracy, robustness, and generalization capabilities. The proposed system generates confusion matrices for class-specific prediction analysis, evaluates both training and testing accuracy, and provides comparative accuracy visualization to help practitioners identify discrepancies between model learning and generalization. The framework incorporates learning curves that analyse accuracy variations as training data increases, enabling detection of underfitting and overfitting issues. Implementation using logistic regression on the Iris dataset demonstrates the framework's effectiveness, achieving 100% training accuracy and 97% testing accuracy. The methodology is designed to be adaptable across different models and datasets, providing a standardized approach for model evaluation. By combining quantitative metrics with visual insights, this framework facilitates informed decision-making in machine learning model development and deployment.