ml4t-shap-analysis skillA
ml4t-shap-analysis is agent-read markdown (skill) from ml4t/skills: Explain model predictions with SHAP values instead of biased built-in feature importance. Use when interpreting which features drive model decisions..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# SHAP Analysis
Built-in `feature_importances_` in tree models is biased toward high-cardinality and correlated features. SHAP values provide additive, consistent feature attributions grounded in game theory.
## The Problem
LightGBM's `feature_importances_` (gain or split-based) systematically overweights features with more unique values and features that are correlated with other predictors. Two features with identical predictive power but different cardinality will show different importance. This leads to incorrect feature selection, misleading model narratives, and poor decisions about which signals to keep or discard. SHAP values are the only feature attribution method that satisfies both local accuracy (attributions sum to prediction) and consistency (a feature that contributes more never gets lower attribution).
## The Pattern
### WRONG
```python
import lightgbm as lgb
model = lgb.LGBMRegressor().fit(X_train, y_train)
# Built-in importance - biased toward high-cardinality features
importance = model.feature_importances_
for name, imp in sorted(zip(feature_names, importance), key=lambda x: -x[1])[:5]:
print(f"{name}: {imp}")
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-shap-analysis@git:20260901.c415df0mdr add ml4t/skills/ml4t-shap-analysis@sha256:232857d846d3da9dPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_kfoxgkpp2dtyqk7b)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 4,853 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,865 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (4853 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
ml4t/skills · 19 stars · license Apache-2.0 · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_kfoxgkpp2dtyqk7b GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-shap-analysis GET https://markdownregistry.com/api/v1/blob/232857d846d3da9d814b54d9486f97f6ba0f871276b0100c175beca3782cf571
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