The Cross-Validation Performance Tracker is a powerful tool for evaluating the robustness of machine learning models. By simulating k-fold cross-validation, it provides insights into model performance across different subsets of data. This tool is designed to be intuitive, user-friendly, and visually engaging.

Key Features

1. Interactive Input Fields: Users can input the number of folds, accuracy, precision, recall, and F1 score for each fold.

2. Real-Time Calculation: Instantly computes the average performance metrics across all folds.

3. Visual Performance Chart: Displays the performance metrics for each fold in a colorful and easy-to-understand format.

4. PDF Download: Generates a downloadable PDF report with the performance metrics and chart.

5. Modern Design: Stylish and responsive interface that fits seamlessly into your WordPress Elementor HTML block.

Use Cases

• Model Evaluation: Evaluate the robustness of machine learning models using cross-validation.

• Educational Tool: Teach students about cross-validation and performance metrics.

• Research: Use in academic or professional research to validate model performance.

How It Works

1. Input Values: Enter the number of folds and the performance metrics (accuracy, precision, recall, F1 score) for each fold.

2. Generate Performance Chart: The tool calculates and displays the performance metrics for each fold and the overall average.

3. View Results: The performance metrics and chart are displayed in a clear and visually appealing format.

4. Download PDF: Click the “Download PDF” button to save the results for future reference.