What Did we Learn and What to Expect in Assignment 5

Module Learning Outcomes

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

• Identify when to implement feature transformations such as imputation and scaling.
• Apply sklearn.pipeline.Pipeline to build a machine learning pipeline.
• Use sklearn for applying numerical feature transformations on the data.
• Discuss the golden rule in the context of feature transformations.
• Carry out hyperparameter optimization using sklearn’s GridSearchCV and RandomizedSearchCV.
• Explain overfitting on the validation set.

The assignment will concentrate on the learning objectives as well as building knowledge on existing concepts.

On to Assignment 5!