lib-shap

Name: lib-shap
Availability: InStock
Author: biomaps-infra

Generate SHAP values for feature importance and prediction explanations.

Updated Mar 13, 2026

Search Tags:#machine learning #xai #model interpretability #feature importance #shap #explainable ai

Ready to run

npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-shap

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lib-shap
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-shap
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-shap

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill demystifies machine learning models by explaining their predictions and feature importance, making complex AI behavior understandable and actionable.

Core Features & Use Cases

Feature Importance: Identify which features most influence model predictions.
Prediction Explanation: Understand why a model made a specific decision for an individual instance.
Model Debugging: Detect biases, validate behavior, and identify potential issues.
Use Case: You have a credit risk model. Use this Skill to explain why a specific applicant was denied, highlighting the key factors (e.g., credit score, debt-to-income ratio) that led to the decision.

Quick Start

Use the lib-shap skill to generate a beeswarm plot for the SHAP values of the test dataset.

Frequently Asked Questions about lib-shap

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

FAQPage Schema

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

You explain machine learning model predictions by computing SHAP values to decompose individual predictions and quantify feature importance. This approach translates complex model behavior into understandable, actionable insights for any black-box algorithm.

Can I use SHAP explainers with tree-based models and deep learning frameworks?▼

Yes, SHAP explainers support tree-based models, deep learning networks, and linear models through specialized algorithms. These explainers calculate feature attributions across various ML algorithms to ensure accurate model interpretability.

What is the best way to debug model biases and validate prediction behavior?▼

The best way to debug model biases is to generate SHAP visualizations like beeswarm plots for your test dataset. Analyzing these feature importance distributions helps you validate behavior and detect potential issues in model decisions.

How do I understand why a specific applicant was denied by a credit risk model?▼

To understand why an applicant was denied, you use SHAP prediction explanation to decompose the individual decision. This highlights the key factors, such as credit score or debt-to-income ratio, that drove the model's specific outcome.

Do I need specialized explainers for black-box model interpretability?▼

Yes, you need specialized explainers to achieve black-box model interpretability because different ML algorithms require specific SHAP calculation methods. This ensures accurate prediction decomposition and feature importance analysis for complex models.