How do I evaluate the results of data augmentation?

How do I evaluate the results of data augmentation?

To evaluate data augmentation results, you can compare model performance metrics like accuracy, precision, and recall before and after augmentation.

What specific metrics should I focus on?

Focus on metrics such as F1 score, AUC-ROC, and confusion matrix to get a comprehensive view of model performance.

Can I visualize the impact of data augmentation?

Yes, you can use visualizations like ROC curves or precision-recall curves to illustrate the impact of data augmentation on model performance.

Are there any tools that can help with this evaluation?

Yes, tools like TensorBoard, MLflow, and Weights & Biases can help you track and visualize model performance metrics.