model-evaluator

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.