Feature Construction
Feature Construction groups the core ideas a learner needs at the 5 · data preprocessing & feature engineering stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Ratios and ratesRatios and rates is a practical concept within Feature Construction. It helps turn the broader workflow stage “5 · Data Preprocessing & Feature Engineering” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02InteractionsInteractions is a practical concept within Feature Construction. It helps turn the broader workflow stage “5 · Data Preprocessing & Feature Engineering” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03Polynomial featuresPolynomial features changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
04Binning and discretisationBinning and discretisation is a practical concept within Feature Construction. It helps turn the broader workflow stage “5 · Data Preprocessing & Feature Engineering” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Domain aggregatesDomain aggregates is a practical concept within Feature Construction. It helps turn the broader workflow stage “5 · Data Preprocessing & Feature Engineering” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
06Group-level featuresGroup-level features changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
07Feature stores and reuseFeature stores and reuse changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.