Machine Learning: Current capabilities
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Reusable estimation and evaluation workflows
Established · Capability snapshot: 2026-11-03
Common libraries provide classification, regression, clustering and preprocessing workflows. Their practical value includes consistent interfaces and reproducible comparisons, not only larger models. Pipeline design is important: preprocessing learned from the complete dataset can leak information into evaluation.
Scikit-learn: Machine Learning in Python — Pedregosa and colleagues / JMLR · Source date: 2011 · Checked
Common pitfalls and recommended practices — scikit-learn developers · Checked
Prediction sets can express uncertainty
Demonstrated result · 2021 tutorial; assumptions still matter
Conformal prediction offers ways to construct prediction sets with coverage guarantees under stated conditions. The guarantee is not that each individual answer is correct or that arbitrary future distribution shifts are harmless. Professionals must distinguish statistical coverage from domain-specific safety.
A Gentle Introduction to Conformal Prediction — Angelopoulos and Bates / arXiv · Source date: 2021-07-15 · Checked