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bio-machine-learning-prediction-explanation

Maintainer FreedomIntelligence · Last updated April 1, 2026

Explain omics ML predictions with SHAP and feature importance.

OpenClawNanoClawAnalysisReproductionbio-machine-learning-prediction-explanation🧠 bioos extended suitebioos extended bioinformatics suiteexplain

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-machine-learning-prediction-explanation

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

shap_values = explainer(X_test).
print(f'SHAP values shape: {shap_values.values.shape}').

Source Doc

Excerpt From SKILL.md

SHAP TreeExplainer (v0.47+ API)

import shap
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

explainer = shap.TreeExplainer(model)

## Summary Plot (Global Feature Importance)

```python
import shap
import matplotlib.pyplot as plt

## Beeswarm plot: shows impact direction and magnitude

shap.plots.beeswarm(shap_values, max_display=20, show=False)
plt.tight_layout()
plt.savefig('shap_summary.png', dpi=150, bbox_inches='tight')
plt.close()

Use cases

Use bio-machine-learning-prediction-explanation for genomics and bioinformatics workflows.
Apply bio-machine-learning-prediction-explanation to sequencing, variant, or omics analysis tasks.