Feature Engineering in Machine Learning Pipelines
Explore how to design and apply key feature engineering techniques for machine learning pipelines. This lesson helps you create new features using domain expertise, transform time-series data, encode categorical variables, and prepare datasets for effective predictive modeling.
Feature engineering plays a significant role in building high-performing machine learning models. It’s about creating new signals that better represent the underlying patterns in the domain you’re analyzing. In this lesson, we’ll explore how to thoughtfully design features using domain expertise and apply common transformation techniques like encoding, interaction terms, and discretization to prepare customer data for modeling. Let’s get started.
Creating a feature effectively
You’re interviewing with a company that partners with retailers to optimize their operations. How would you use feature engineering to create a new feature, leveraging their retail partners’ domain expertise? You may also receive a follow-up question along the lines of: What kind of data would you need to create this feature? ...