Comparing Supervised ML models This repository serves as a comprehensive resource for analyzing and evaluating various machine learning models. It provides a collection of tools and script to facilitate in-depth analysis, comparison, and interpretation of ML algorithms and models.
Key Features -Model Evaluation: Explore different evaluation metrics to assess the performance of ML models, including accuracy, precision, recall, F1-score, ROC curves, and more.
-Comparative Analysis: Conduct comparative analysis of multiple ML models to identify the most suitable algorithm for your specific use case or dataset.
-Visualizations: Utilize interactive visualizations such as confusion matrices, feature importance plots, and prediction distribution charts to gain insights into model behavior and performance.