Numeric Preprocessing
Numeric Preprocessing 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
StandardisationStandardisation is a practical concept within Numeric Preprocessing. 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.
02Min-max scalingMin-max scaling is a practical concept within Numeric Preprocessing. 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.
03Robust scalingRobust scaling is a practical concept within Numeric Preprocessing. 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.
04MaxAbs scalingMaxAbs scaling is a practical concept within Numeric Preprocessing. 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.
05Log transformsLog transforms 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.
06Power transformsPower transforms 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.
07Quantile transformsQuantile transforms 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.
08When scaling is unnecessaryWhen scaling is unnecessary is a practical concept within Numeric Preprocessing. 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.