Features and Feature Engineering
A feature is a specific measurable aspect or characteristic. Feature engineering decides which ones matter, with three purposes - improve performance (the most important), cut cost, and boost explainability. Feature flags toggle functionality without redeploying.
A Feature is a specific measurable aspect or characteristic → height, colour, substance. Feature engineering decides which ones matter.
For a credit score, a person's age matters more than their height → subject matter experts help pick essential features. Use the same features for training and testing for consistency → and eliminate unnecessary features, which complicate testing and waste resources.
Three purposes of feature engineering:
- Improve performance → the most important purpose → structure datasets so the model optimally learns feature-to-target relationships → curate the subset with the greatest predictive power
- Cut cost, boost effectiveness → fewer features → less data to process, store and send in API calls → better latency → write versioned, tested feature definitions mirrored for training and production
- Boost explainability → the degree someone can consistently predict a model's result → essential for fairness, privacy, reliability, robustness, causality and trust
Feature flags let you disable functionality without redeploying code → introduce features while controlling availability to specific users or groups in production → handy for rollbacks and deployments across jurisdictions with varying requirements.
Key terms - quick answers
What is “Feature”?
What is “Feature engineering”?
What is “Feature flags”?
What is “Explainability”?
Sources and study method
This independent lesson uses active recall, spaced retrieval and scenario practice. Read the full study method.