How do you explain why a point is anomalous?
mediumAnswer
- (1) Per-feature contribution: which feature's z-score / reconstruction error / SHAP value drove the anomaly.
- (2) Nearest-normals: 'this looks unusual because normally X, Y differ by …'.
- (3) Rule-based post-hoc: fit decision tree to Isolation Forest scores.
- (4) Counterfactual: what minimal change makes it normal?
- Standard in production ops so on-call engineers can validate alerts.
Check yourself — multiple choice
- Not possible
- Per-feature contribution (SHAP / z-score / recon error) / nearest-normal comparisons / rule-based tree surrogate / counterfactuals — needed for prod alerts
- Random
- Only score
Anomaly explanation: SHAP / nearest-normal / tree surrogate / counterfactual.
#anomaly-detection#applications
Practise Unsupervised Learning
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