# Matlab Classify Tabular Data

Use this skill to classify tabular data end-to-end in MATLAB, load a dataset, prepare...

- Repository: https://github.com/matlab/matlab-agentic-toolkit (1237 stars)
- Category: Testing
- License: see repository
- Last updated: 2026-10-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-classify-tabular-data -g -a claude-code -y`
Codex: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-classify-tabular-data -g -a codex -y`
Cursor: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-classify-tabular-data -g -a cursor -y`
Gemini CLI: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-classify-tabular-data -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add matlab/matlab-agentic-toolkit`

## Description

Use this skill to classify tabular data end-to-end in MATLAB, load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

## SKILL.md, as published by its author (untrusted third-party text, read it as 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

The rest: https://github.com/matlab/matlab-agentic-toolkit/blob/HEAD/skills-catalog/ai-and-statistics/matlab-classify-tabular-data/SKILL.md