Tag: interpretability

A practical explanation of ML metrics with decision tables for regression tolerance, rare-event classification, threshold tradeoffs, and the failure case where accuracy looked good but the decision failed.

Outcome: Clarified how metric choice, threshold design, tree-based pattern discovery, and logit interpretation affect whether ML outputs are useful for action.