ScriptAI for Codev1.2

Feature Importance Analyzer

by Community · free · Last verified 2026-03-17

Analyzes feature importance for scikit-learn compatible models using multiple advanced techniques. It computes SHAP values with Tree and Kernel Explainers, calculates permutation importance, and performs feature selection with Boruta. Results are compiled into an interactive HTML dashboard for easy interpretation and sharing.

https://github.com/slundberg/shap ↗

B—Above Average

Adoption: AQuality: AFreshness: ACitations: B+Engagement: F

Specifications

License: MIT
Pricing: free
Capabilities: Global feature importance using SHAP summary plots, Local, instance-level explanations with SHAP force plots, Model-agnostic permutation importance calculation, All-relevant feature selection using the Boruta algorithm, Visualization of feature interaction effects via heatmaps, Generation of self-contained, interactive HTML dashboards, Compatibility with any model following the scikit-learn API, Exporting ranked feature importance data to CSV files, Support for both classification and regression tasks
Integrations: [object Object], [object Object], [object Object], [object Object], [object Object]
Use Cases: [object Object], [object Object], [object Object], [object Object]
API Available: No
Language: python
Dependencies: shap, scikit-learn, plotly, pandas, boruta
Environment: Python 3.10+
Est. Runtime: 2-15 minutes
Tags: feature-importance, shap, permutation-importance, explainability, xai, model-interpretation, scikit-learn, boruta, data-science, machine-learning, model-debugging, reporting
Added: 2026-03-17
Completeness: 0.7%

Index Score

66.9

Adoption

Quality

Freshness

Citations

Engagement

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