Compare Classification Models with Statistical Uncertainty

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

• Response is continuous — use a regression skill instead.
• Predictors are images, sequences, or time series — this skill assumes a numeric matrix or a table of scalar predictors.
• User wants to design or train a specific neural network architecture — use matlab-train-network. This skill does include fitcnet as one candidate on regular-branch data, but does not tune network layers or hyperparameters.
• User only wants to score a pretrained model on new data — this skill trains and compares; scoring an existing model does not need it.
• User wants cost-sensitive learning or a custom class-prior vector. Refuse plainly and stop — do not attempt a workaround. This skill's only supported prior control is the built-in uniform-prior toggle for imbalanced data ('Prior','uniform', offered via the UseUniformPrior flag in Step 5). Arbitrary 'Prior',[...] vectors and 'Cost',C matrices are not supported: neither the model-definition helper nor the CV/holdout scoring helpers thread these through, the branch tables and ECOC-expansion logic assume the built-in prior/cost defaults, and pairwise statistical tests (testckfold, testcholdout) score misclassification rate rather than expected cost. Attempting to bypass the helpers to inject a custom prior or cost is a hard refusal, not a judgment call — tell the user: "This skill does not support custom class priors or cost matrices. If you need cost-sensitive learning or a specific prior vector, use fitc* directly with the 'Prior' or 'Cost' name-value pair; this skill's statistical-comparison workflow will not give correct results in that setting."

Running MATLAB

Run all MATLAB code via the MATLAB MCP server (mcp__matlab__evaluate_matlab_code, or mcp__matlab__run_matlab_file for scripts). Set project_path to this skill's scripts/helpers/ directory so the workflow helpers resolve on the current working folder without any addpath calls.

Communication style while running this skill

Talk to the user about their data and results, not about the skill's plumbing. Everything under references/ and scripts/ (both helpers/ and model_catalog/) is internal — a user watching the run should never have to ask what a filename means.

Concretely, while executing this skill:

• Do not name internal files or helpers in progress messages. references/select-classifiers.md, build_model_definitions, compute_pairwise_pvalues_cv, classifier_branches, resolve_recipe, modelDefs, cvFitFcn, etc., are all internal. If you must reference them (e.g., surfacing a bug the user can act on), name them once and explain what they are.
• Do not narrate branch dispatch or filter decisions by name. "Dispatching to the wide branch", "applying the imbalanced overlay", "isSparse is false so we skip the sparse branch" — all internal. The user only needs to hear the outcome: "This dataset is wide (200 features, 40 samples), so I'm using linear models."
• Do not read reference files out loud. When a step says STOP and read references/foo.md, that is a directive to you, not a status update to broadcast. Read it silently and continue.
• Do announce what the user chose to run, and roughly how long it will take, before a long training loop or HPO run. One sentence.
• Do surface user-facing questions verbatim where the step specifies them (evaluation strategy, interpretability, uniform prior, HPO selection). Those are the user's interface to the skill.
• Do surface a real problem if one appears — a failed check gate, a branch that couldn't match, a fit call that errored. Name it plainly; then, and only then, is it fine to reference the internal file where the fix belongs.

Illustrative contrast: