Load when the user is comparing Bayesian models, computing LOO-CV / ELPD, calling arviz_stats.loo or arviz_stats.compare, doing model stacking/averaging, or computing Bayes factors. Covers the ArviZ 1.1 LOO/ELPD/stacking APIs exclusively (no waic). Triggers include: model comparison, LOO, ELPD, compare, loo_expectations, loo_metrics, loo_r2, Pareto k, stacking, Bayes factor, cross-validation, predictive accuracy, information criterion.
How to install model-evaluation?
pymc-labs/python-analytics-skills/model-evaluation
$npx -y skills add pymc-labs/python-analytics-skills --skill model-evaluationInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/pymc-labs/python-analytics-skills" --skill "pymc-labs/python-analytics-skills/model-evaluation"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/pymc-labs/python-analytics-skills" that are relevant to the current task. Run `npx skills add "https://github.com/pymc-labs/python-analytics-skills"` and select the relevant skills, then follow their instructions.
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