matlab-classify-tabular-data

# 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:

> **Don't:** "Reading `references/dataprep.md`... computing `flags` via `compute_data_flags`... `isImbalanced=true`, so reading `references/select-classifiers-imbalanced.md` and applying `IMBALANCED_BOOSTING_MODELS` overlay to `build_model_definitions`. `modelDefs` now has 8 entries including RUSBoost-OVO and RUSBoost-OVA."
>
> **Do:** "Class ratio is 9:1, so I'll use imbalance-aware boosting models. Before I train, I need to ask you about the class prior — [uniform-prior question verbatim]."

Rule of thumb: if a sentence would only make sense to someone who has read this skill's source files, don't say it.

## Before Writing Code

**Do not reuse any variables from previous analysis runs.** Always execute the full prescription and set all variables from scratch for each analyzed dataset. Every step must define its own variables — never assume anything remains in the workspace from a prior run.

**Define `skillPath` up front.** Several helpers take `skillPath` as an argument (the skill's root directory, two levels above `scripts/helpers/`). When