What Did we Learn and What to Expect in Assignment 7

Module Learning Outcomes

By the end of the module, students are expected to:

• Explain why accuracy is not always the best metric in ML.
• Explain components of a confusion matrix.
• Define precision, recall, and f1-score and use them to evaluate different classifiers.
• Identify whether there is class imbalance and whether you need to deal with it.
• Explain class_weight and use it to deal with data imbalance.
• Appropriately select a scoring metric given a regression problem.
• Interpret and communicate the meanings of different scoring metrics on regression problems. MSE, RMSE, R², MAPE.
• Apply different scoring functions with cross_validate and GridSearchCV and RandomizedSearchCV.

On to Assignment 7!