Evaluation metrics for generative models

A machine learning algorithm is said learn task T from experience E, if its performance measured using performance measure P improves for T with E.

To measure the performance of models there have been many quantitative techniques suggested in past literature for different task such as:-

• Confusion matrix, AUC, PR, F1-score and accuracy for classification task
• MAE, MSE, RMSE, R2 and adj-R2 for regression task
• Dunn’s index and silhouette coefficient for Clustering task