Dimensionality Reduction
Dimensionality Reduction 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
PCA intuitionPCA intuition is a practical concept within Dimensionality Reduction. 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.
02Choosing number of componentsChoosing number of components is a practical concept within Dimensionality Reduction. 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.
03Explained varianceExplained variance is a practical concept within Dimensionality Reduction. 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.
04Truncated SVDTruncated SVD is a practical concept within Dimensionality Reduction. 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.
05t-SNE for visualisationt-SNE for visualisation is a communication and diagnostic technique that maps data or results into a visual form. A useful visual makes comparisons easy, exposes uncertainty and supports the analytical question rather than merely decorating the report.
06UMAP for visualisationUMAP for visualisation is a communication and diagnostic technique that maps data or results into a visual form. A useful visual makes comparisons easy, exposes uncertainty and supports the analytical question rather than merely decorating the report.
07Fit transforms inside training foldsFit transforms inside training folds 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.