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name: evaluation-and-explainability description: "Use Darts deterministic/probabilistic metrics, reductions, anomaly/classification evaluation, and SHAP explainability safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Evaluation and explainability

Use this sub-skill when the user needs Darts metrics, deterministic vs stochastic forecast evaluation, reduction/aggregation behavior, anomaly/classification metrics, SHAP explainability, or headless plotting safety.

Read first

• references/workflows.md for deterministic metrics, probabilistic quantile/interval metrics, reductions, and SHAP setup.
• references/api-reference.md for metric families, q vs q_interval, and output-shape controls.
• references/troubleshooting.md for metric shape, stochastic sample, SHAP background, unsupported model, and plotting failures.
• scripts/evaluation_smoke.py for a tiny metrics and optional SHAP-signature smoke.

Route by task

• Point forecast metrics: use deterministic metrics such as mae, rmse, mape, or r2_score on actual and deterministic forecast series.
• Stochastic forecast metrics: use quantile or interval metrics on forecasts with samples; distinguish q from q_interval.
• Keep per-component output: set reductions deliberately instead of accepting default scalar reductions.
• Evaluate anomaly outputs: use continuous-score metrics for scores and binary/classification metrics for detector flags.
• Explain a fitted model: use Darts explainers such as ShapExplainer only for supported, fitted models with bounded background/foreground data.
• Need model training first: route to forecasting, torch, or anomaly sub-skills.

Safe check

python scripts/evaluation_smoke.py --check-shap

The smoke uses generated data, finite metric assertions, and a SHAP import/signature check. It does not open plots or notebooks.

Boundaries

This sub-skill evaluates and explains outputs; it does not choose/fit the forecasting model or build covariates. Avoid unconditional plotting in headless environments. Do not use source-repo notebooks as runtime dependencies.