How can I evaluate data augmentation techniques?

What are some effective ways to evaluate data augmentation techniques?

You can evaluate data augmentation techniques by analyzing model performance metrics, visualizing augmented data, and conducting ablation studies.

What metrics should I focus on when evaluating these techniques?

Focus on metrics like accuracy, precision, recall, F1 score, and the model's generalization ability on unseen data.

Can you explain what an ablation study is?

An ablation study involves systematically removing or altering components of your model or data augmentation techniques to assess their impact on performance.

How do I visualize augmented data effectively?

You can use techniques like t-SNE or PCA for dimensionality reduction, and then plot the augmented data to see how it compares to the original.