Matlab Interpret Machine Learning Model
Interpret and explain a trained tabular machine-learning model (classification or...
Install
npx skills add matlab/matlab-agentic-toolkit --skill matlab-interpret-machine-learning-model -g -a claude-code -y- Repository
- matlab/matlab-agentic-toolkit1.2k
- Category
- Media
- Updated
- 8d ago
- License
- See repository
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
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.
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
dlnetworkor a prediction function handle)
