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inbharatai
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model-evaluator

Evaluate machine learning models with cross-validation, confusion matrices, and ROC curves.

22|8|Updated Mar 14, 2026
Search Tags:#data science#machine learning#performance metrics#model evaluation#interpretability#bias audit
One-click install
npx skills add https://github.com/inbharatai/claude-skills --skill model-evaluator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-evaluator
Source: https://github.com/inbharatai/claude-skills/tree/main/skills/model-evaluator
Command: npx skills add https://github.com/inbharatai/claude-skills --skill model-evaluator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sklearn, shap, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the critical need for thorough and systematic evaluation of machine learning models, ensuring their reliability, fairness, and performance.

Core Features & Use Cases

  • Performance Metrics: Generates key metrics like accuracy, precision, recall, F1-score, and AUC.
  • Visualizations: Creates confusion matrices and ROC curves for visual analysis.
  • Bias Audits: Assesses models for fairness across different demographic groups.
  • Interpretability: Provides insights into model predictions using techniques like SHAP.
  • Use Case: After training a classification model, use this Skill to generate a comprehensive evaluation report including performance metrics, a confusion matrix, and an analysis of potential biases.

Quick Start

Evaluate the attached model using cross-validation and generate a confusion matrix.

Frequently Asked Questions about model-evaluator

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

FAQPage Schema
How do I evaluate machine learning model performance using cross-validation and confusion matrices?▼

To evaluate machine learning model performance, the Skill uses cross-validation and confusion matrices to assess accuracy, precision, recall, and F1-score. This generates a comprehensive report on classification reliability and production readiness.

What is a bias audit for machine learning models and when do I need one?▼

A bias audit assesses machine learning models for fairness across different demographic groups to ensure equitable predictions. You need a bias audit when preparing models for production to verify fairness, reliability, and explainability.

How do I interpret model predictions using SHAP values in scikit-learn?▼

To interpret model predictions using SHAP values, the Skill integrates with scikit-learn models to provide explainability insights. This reveals feature importance and clarifies how specific input variables drive individual model predictions.

Do I need scikit-learn and SHAP installed to run model evaluation and ROC curve analysis?▼

Yes, you need scikit-learn and SHAP installed as dependencies to run model evaluation and ROC curve analysis. These Python libraries provide the foundational algorithms for performance metrics, visualizations, and interpretability.

What's the best way to assess model fairness and explainability for production readiness?▼

The best way to assess model fairness and explainability for production readiness is combining bias audits with SHAP interpretability methods. This systematic approach evaluates performance metrics while ensuring equitable outcomes across demographic groups.

Why does my machine learning model evaluation only show accuracy without AUC or recall metrics?▼

Machine learning model evaluation requires comprehensive metric generation beyond simple accuracy, including AUC, recall, precision, and F1-score. Using advanced techniques like ROC curves and cross-validation provides a complete picture of model performance.

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