ML model explanation — feature importance, SHAP-style attribution, LIME and partial dependence
Explain ML predictions with feature importance, a from-scratch SHAP-style calculator, a local LIME surrogate, partial dependence and sensitivity plots
- What
- Explain ML predictions with feature importance, a from-scratch SHAP-style calculator, a local LIME surrogate, partial dependence and sensitivity plots
- Cost
- Free
- Needs
- Python with scikit-learn, numpy, pandas, matplotlib, seaborn — a trained model or sample dataset to explain
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @aj-geddes's ML model explanation skill: a hands-on walkthrough of the explainability toolkit. Covers feature importance (impurity vs permutation), a from-scratch SHAP-style feature attribution calculator, a local LIME surrogate model, partial dependence plots, global behavior analysis, and feature sensitivity testing — with ready-to-run Python code and visualization recipes for each technique, plus a comparison table (speed, scope, cost) and regulatory context (GDPR right to explanation, fair lending, healthcare transparency). Honest caveats: the skill implements simplified from-scratch approximations of SHAP and LIME rather than the real libraries — excellent for learning the mechanics, not a drop-in for production explainability (use the `shap` package for real work). MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: Python with scikit-learn, numpy, pandas, matplotlib, seaborn — a trained model or sample dataset to explain Install "ML model explanation — feature importance, SHAP-style attribution, LIME and partial dependence" for me. It gives my agent @aj-geddes's explainability walkthrough: feature importance (impurity vs permutation), a from-scratch SHAP-style attribution calculator, a local LIME surrogate, partial dependence plots, prediction-distribution and feature-sensitivity analysis, and visualization recipes, with a techniques comparison table and regulatory context. IMPORTANT: the skill uses simplified from-scratch SHAP/LIME approximations for learning — for production use, install the real shap library instead. MIT-licensed. Repository: https://github.com/aj-geddes/useful-ai-prompts/blob/main/skills/ml-model-explanation/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "ml-model-explanation". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. pip install scikit-learn pandas matplotlib seaborn and point the agent at my trained model or dataset; nothing else — it's a walkthrough with runnable code). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.