Use when building QSAR/ML models that need calibrated uncertainty estimates. Covers epistemic vs aleatoric uncertainty theory, conformal prediction with MAPIE (guaranteed coverage), Gaussian processes with Tanimoto kernel, deep uncertainty (MC dropout, deep ensembles, Laplace), and applicability domain (AD) assessment. Critical for active learning and reliable property prediction.
How to install uncertainty-qsar?
kdevos12/alkyl/uncertainty-qsar
$npx -y skills add kdevos12/alkyl --skill uncertainty-qsarInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/kdevos12/alkyl" --skill "kdevos12/alkyl/uncertainty-qsar"` 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/kdevos12/alkyl" that are relevant to the current task. Run `npx skills add "https://github.com/kdevos12/alkyl"` and select the relevant skills, then follow their instructions.
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