Compare Classification Models with Statistical Uncertainty

Compare classifiers on the user's dataset and identify the top tier of models that are statistically equivalent in accuracy.

This skill bundles the workflow in references/ (step-by-step markdown instructions read on demand) and scripts/ (MATLAB code). Under scripts/, helpers/ holds the reusable computation and plotting functions the agent calls directly, and model_catalog/ holds the declarative branch-table .m files invoked internally by build_model_definitions via run(...). Do not invoke files in references/ as separate skills — they are only loaded via the Read tool when the corresponding step runs. See references/README.md for the layout.

When to Use

• User wants to classify tabular data (matrix or table of predictors + categorical response).
• User asks to compare multiple classifiers, pick the best model, or evaluate classifier accuracy.
• User needs cross-validation, a holdout evaluation, or hyperparameter optimization for classifiers.
• User needs statistical tests (McNemar, 5×2 cv, Friedman) to know which accuracy differences are significant.

When NOT to Use