Feature Selection
Select the most important features: filter methods (mutual information, chi-squared), wrapper methods (recursive feature elimination), embedded methods (LASSO, tree importance), and SHAP-based selection. Use when reducing dimensionality, improving model performance, or identifying key drivers.
Install
$ agentstack add skill-ericwang915-data-scientist-skills-feature-selection ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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How agent discovery & health will work →About
Feature Selection
Purpose
Select the most informative features to improve model performance, reduce overfitting, and increase interpretability.
How It Works
Method Categories
| Category | Methods | Speed | Considers Interactions? | |----------|---------|-------|------------------------| | Filter | Mutual info, chi², variance threshold | Fast | No | | Wrapper | RFE, forward/backward selection | Slow | Yes | | Embedded | LASSO, tree importance, elastic net | Medium | Partially | | SHAP-based | SHAP importance, Boruta-SHAP | Slow | Yes |
Step-by-Step
- Remove zero-variance and near-zero-variance features
- Remove highly correlated pairs (keep the one with higher target correlation)
- Apply filter methods for initial ranking
- Use embedded methods (LASSO, tree importance) for refined selection
- Validate with RFE or SHAP for final feature set
- Compare model performance: all features vs. selected features
Usage Examples
"I have 200 features. Select the 20 most important for predicting churn.""Which features can I drop without hurting model accuracy?"Output Format
- Feature Ranking: Importance scores per feature
- Selected Features: Final set with rationale
- Comparison: Model performance with all vs. selected features
- Python Code: sklearn / SHAP implementation
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: ericwang915
- Source: ericwang915/data-scientist-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.