# Matlab Interpret Machine Learning Model

Interpret and explain a trained tabular machine-learning model (classification or...

- Repository: https://github.com/matlab/matlab-agentic-toolkit (1237 stars)
- Category: Media
- 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-interpret-machine-learning-model -g -a claude-code -y`
Codex: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-interpret-machine-learning-model -g -a codex -y`
Cursor: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-interpret-machine-learning-model -g -a cursor -y`
Gemini CLI: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-interpret-machine-learning-model -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add matlab/matlab-agentic-toolkit`

## Description

Interpret and explain a trained tabular machine-learning model (classification or regression) in MATLAB. Find which predictors, features, or columns matter most; explain why the model made a specific prediction, including diagnosing predictions it got wrong; show how a predictor affects the output; and compare how the model behaves across cohorts or subgroups. Uses model-agnostic techniques and model-native measures, and works on custom models (such as a dlnetwork) through a prediction function handle. Use for model interpretability, explainability, and feature-importance questions on tabular data, not for training, tuning, feature selection, deploying models, or models trained on image, text, signal, or other non-tabular data.

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

# Interpret a Trained Tabular Model

Interpret a trained tabular model, a built-in Statistics and Machine Learning Toolbox (SMLT) classifier or regressor, or a custom model reached through a prediction function handle, by matching the user's intent to the right techniques, handling the custom-model case correctly, and avoiding the failure modes agents fall into on this workflow. This skill bundles the workflow in `references/` (per-technique procedures and branch tables read on-demand) and `scripts/` (the `functionHandleMap` helper). Do not invoke files in `references/` as separate skills, they are only read when the corresponding step runs.

## When to Use

- "Which predictors matter / feature importance / what drives the model"
- "Why did the model predict X for this case / explain this prediction"
- "What shape is the effect of predictor P / partial dependence / how does the prediction change with P"
- "Why did the model get these cases wrong / diagnose these errors / what would flip this"
- "Does the model behave differently for group Y / segment / cohort"
- Any of the above over a **custom / non-SMLT model** (a `dlnetwork` or a prediction function handle)

## When NOT to Use

The rest: https://github.com/matlab/matlab-agentic-toolkit/blob/HEAD/skills-catalog/ai-and-statistics/matlab-interpret-machine-learning-model/SKILL.md