Model Selection Practice
Model Selection Practice groups the core ideas a learner needs at the 8 · hyperparameter optimisation & model selection 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
Choose a primary metricChoose a primary metric is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
02Multi-metric evaluationMulti-metric evaluation is an evaluation quantity that compresses a particular aspect of predictive behaviour into a number. Its usefulness depends on whether that aspect matches the real decision cost, class prevalence and intended model output.
03Complexity vs performanceComplexity vs performance is a practical concept within Model Selection Practice. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” 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.
04Latency and memory constraintsLatency and memory constraints belongs to the operational phase where an analytical result becomes a maintained system. Production quality requires the data contract, preprocessing, model, decision logic and monitoring to remain consistent over time.
05Stability across foldsStability across folds is a practical concept within Model Selection Practice. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” 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.
06One-standard-error rule intuitionOne-standard-error rule intuition is a practical concept within Model Selection Practice. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” 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.
07Final refit and untouched test setFinal refit and untouched test set is a practical concept within Model Selection Practice. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” 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.