Model-Agnostic Explainability
Model-Agnostic Explainability groups the core ideas a learner needs at the 10 · interpretation & explainability 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
Permutation importancePermutation importance is a practical concept within Model-Agnostic Explainability. It helps turn the broader workflow stage “10 · Interpretation & Explainability” 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.
02Partial dependencePartial dependence is a practical concept within Model-Agnostic Explainability. It helps turn the broader workflow stage “10 · Interpretation & Explainability” 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.
03ICE plotsICE plots 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.
04SHAP intuitionSHAP intuition is a practical concept within Model-Agnostic Explainability. It helps turn the broader workflow stage “10 · Interpretation & Explainability” 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.
05LIME intuitionLIME intuition is a practical concept within Model-Agnostic Explainability. It helps turn the broader workflow stage “10 · Interpretation & Explainability” 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.
06Counterfactual explanationsCounterfactual explanations is a practical concept within Model-Agnostic Explainability. It helps turn the broader workflow stage “10 · Interpretation & Explainability” 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.
07Correlated-feature caveatsCorrelated-feature caveats 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.