Scoring and evaluation

After you have a trained machine learning model, test the model against a test subset to evaluate how well it performs.

Score and evaluate the model on the test data subset.

Understand differences between train and test set distributions. How is unseen data different than what you used in training?

Revisit model evaluation metric; ensure that this metric drives desirable downstream user behavior.