ML Augmentation Metrics

ML Augmentation metrics capture the value of using synthetic data for the purposes of training an ML model. They assume that you are augmenting the real data with synthetic data to create a more enhanced training set for solving an ML problem.

We hope that the augmented data (real + synthetic) will yield a better ML model than just using the real data by itself. This comparison captures the value of adding synthetic data into your ML workflow. These metrics will allow you to measure the impact of synthetic data.

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Apply these metrics to evaluate the utility of synthetic data for ML augmentation:

• BinaryClassifierPrecisionEfficacy: Use augmented data to train a binary classifier, optimized for precision

• BinaryClassifierRecallEfficacy: Use augmented data to train a binary classifier, optimized for recall