bio-machine-learning-prediction-explanation
Explains machine learning predictions on omics data using SHAP values and LIME for feature attribution. Identifies which genes or features drive classifier decisions. Use when interpreting biomarker classifiers or understanding model predictions.
npx skills add BioTender-max/awesome-bio-agent-skills --skill prediction-explanation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scikit-learn 1.4+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Model Interpretation for Omics Classifiers **"Which genes drive my classifier's predictions?"** → Compute per-feature attribution scores using SHAP values or LIME to explain which genes or features contribute most to model decisions. - Python: `shap.TreeExplainer(model).shap_values(X)`, `lime.lime_tabular.LimeTabularExplainer()` ## SHAP TreeExplainer **Goal:** Compute exact SHAP values for tree-based models to quantify each feature's contribution to predictions. **Approach:** Use TreeExplainer for polynomial-time exact Shapley value computation on Random Forest or boosted tree models. ```python import shap from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train)
- Version Compatibility
- SHAP TreeExplainer
- Summary Plot (Global Feature Importance)
- Force Plot (Individual Prediction)
- SHAP for XGBoost
- LIME (Local Interpretable Model-agnostic Explanations)
- Extract Top Features from SHAP
- Dependence Plot (Feature Interactions)
- Multi-class SHAP
- Related Skills
What does the bio-machine-learning-prediction-explanation skill do?
Explains machine learning predictions on omics data using SHAP values and LIME for feature attribution. Identifies which genes or features drive classifier decisions. Use when interpreting biomarker classifiers or understanding model predictions.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill prediction-explanation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.