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scientific-feature-importance

Quantify feature contributions using MDI and permutation importance with Python.

3|1|Updated Feb 11, 2026

Search Tags:#visualization #machine-learning #explainability #feature-importance #multi-target #permutation-importance #PDP

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-feature-importance

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-feature-importance
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-feature-importance
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-feature-importance

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill quantifies the contribution of individual features to machine learning model predictions, enabling clearer model explanations by combining Tree-based Feature Importance (MDI) and Permutation Importance.

Core Features & Use Cases

Tree-based Feature Importance (MDI) to identify influential features across models.
Permutation Importance to validate feature impact with model-agnostic assessment.
Multi-target importance panels and partial dependence plots to compare across targets and inspect feature effects.
Output CSVs and figures for documentation and reporting.

Quick Start

Train a model on your dataset and run the provided analysis functions to generate feature-importance figures and the results table.

Frequently Asked Questions about scientific-feature-importance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I explain machine learning model predictions using feature importance?▼

You can explain machine learning model predictions by quantifying individual feature contributions using tree-based importance (MDI) and permutation importance, generating visualizations and result tables for robust explanations.

What is the difference between MDI and permutation importance for model explainability?▼

MDI calculates tree-based feature importance internally, while permutation importance provides a model-agnostic assessment by validating feature impact, allowing you to compare and cross-check feature influence across datasets.

Can I generate partial dependence plots for multiple targets in one analysis?▼

Yes, you can generate partial dependence plots and multi-target importance panels to inspect feature effects and compare feature importance contributions across multiple targets simultaneously.

How do I output feature importance results for reporting and documentation?▼

You can output feature importance data, visualizations, and results tables as CSVs and figures, providing structured outputs directly suited for downstream documentation and reporting.

Do I need a pre-trained model to calculate permutation importance?▼

Yes, you need a pre-trained model on your dataset to run the provided analysis functions, which then calculate permutation importance and generate the corresponding feature importance figures and tables.