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name: evaluation-and-uncertainty description: "Validation, cross-validation, metrics, quantile regression, conformal prediction, and uncertainty evaluation workflows for NeuralProphet." disable-model-invocation: true metadata: disco-role: operating license: MIT

Evaluation and uncertainty

Use this sub-skill when the task mentions train/validation/test splits, holdout metrics, rolling cross-validation, metric collection, quantile forecasts, prediction intervals, split conformal prediction, or uncertainty evaluation.

Route map

Stay here for:

• split_df, sequential train/calibration/test splits, and validation passed through fit(validation_df=...).
• Holdout test(...) metrics after fitting a model.
• Rolling folds from crossvalidation_split_df(...) and advanced validation/test fold sets from double_crossvalidation_split_df(...).
• Constructor quantiles, quantile-forecast columns, conformal_predict(...), and uncertainty_evaluate(...).

Route elsewhere inside this skill graph when the task is mainly about:

• Core ds/y dataframe preparation, basic fit, predict, or future dataframe construction: ../core-forecasting/SKILL.md.
• Trend, seasonality, autoregression, lagged regressors, future regressors, events, holidays, or global/local model configuration: ../components-and-exogenous/SKILL.md.
• Plotting backends, interval plots, save/load, CLI/version checks, logging, accelerator/trainer operations, or TorchProphet migration: ../operations-and-migration/SKILL.md.

Start here

1. For holdout validation/test and rolling backtests, follow references/evaluation-workflows.md.
2. For exact method signatures, output columns, and parameter meanings, use references/api-reference.md.
3. For quantile, conformal, split leakage, missing metric, and expensive-CV recovery, use references/troubleshooting.md.
4. To prove the installed package can run a tiny CPU uncertainty path without network access, run python scripts/smoke_uncertainty.py from this sub-skill directory or pass the script path to Python from any working directory.

Assume evaluation dataframes already contain the required ds, y, optional ID, and any configured exogenous columns. This sub-skill explains how to split and evaluate them; it does not redefine the core dataframe schema or component construction contracts.