Regression Metrics
Regression Metrics groups the core ideas a learner needs at the 9 · evaluation, metrics & diagnostics 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
MAEMAE is a practical concept within Regression Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” 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.
02MSEMSE is a practical concept within Regression Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” 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.
03RMSERMSE is a practical concept within Regression Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” 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.
04R-squaredR-squared is a practical concept within Regression Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” 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.
05Adjusted R-squaredAdjusted R-squared is a practical concept within Regression Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” 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.
06MAPEMAPE is a practical concept within Regression Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” 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.
07sMAPEsMAPE is a practical concept within Regression Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” 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.
08MSLE and RMSLEMSLE and RMSLE is a practical concept within Regression Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” 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.
09Median absolute errorMedian absolute error is a practical concept within Regression Metrics. It helps turn the broader workflow stage “9 · Evaluation, Metrics & Diagnostics” 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.
10Huber lossHuber loss 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.