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shap

Explain machine learning predictions by quantifying feature importance and detecting bias.

Updated Aug 23, 2026
Search Tags:#machine learning#feature importance#model debugging#bias detection#explainability#model interpretation
One-click install
npx skills add https://github.com/ogngnaoh/scientific-agent-skills --skill shap-ogngnaoh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shap
Source: https://github.com/ogngnaoh/scientific-agent-skills/tree/main/scientific-agent-skills/skills/shap
Command: npx skills add https://github.com/ogngnaoh/scientific-agent-skills --skill shap-ogngnaoh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires shap, matplotlib, numpy, pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides detailed, transparent explanations of machine learning model predictions, clarifying how each feature influences outcomes.

Core Features & Use Cases

  • Global Model Interpretation: Determine which features are most important across the entire dataset.
  • Local Prediction Breakdown: Understand the contribution of each feature to individual predictions.
  • Model Debugging and Validation: Identify potential issues or biases by analyzing feature impact patterns.
  • Use Case: When deploying a customer churn model, use this Skill to explain why specific users are flagged as likely to leave, based on their feature values.

Quick Start

Explain the prediction of a trained model for a specific input to gain instant insight into feature contributions.

Frequently Asked Questions about shap

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

FAQPage Schema
How do I interpret machine learning predictions and understand feature importance?▼

Interpreting machine learning predictions involves quantifying feature impact to clarify how each variable influences outcomes, enabling analysts to debug models, detect bias, and validate prediction logic globally and locally.

What's the best way to explain individual model predictions for customer churn?▼

Explaining individual predictions for customer churn requires local prediction breakdowns that quantify the specific contribution of each feature value to a single outcome, identifying exactly why a user is flagged as likely to leave.

Does this model interpretation approach support tree-based models and neural networks?▼

This model interpretation approach supports a wide variety of model types including tree-based models, neural networks, and linear models by using model-specific explainers to generate accurate feature attributions.

Can I use pandas and matplotlib to visualize global feature importance across a dataset?▼

You can visualize global feature importance across a dataset using pandas and matplotlib, which are required Python visualization libraries for processing data and generating visual attributions for model analysis.

How do I detect bias and debug a machine learning model using feature attributions?▼

Detecting bias and debugging a machine learning model involves analyzing feature impact patterns globally to identify potential issues, validating that outcomes are not improperly skewed by specific feature values.

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