Model Evaluator

What it does

The Model Evaluator skill provides a framework for rigorously assessing and comparing machine learning model performance across various dimensions. It guides users through designing evaluation protocols, selecting appropriate metrics, and performing statistical analysis to ensure reliable conclusions about model selection. The skill supports evaluating models like LLMs, classifiers, and custom models, focusing on aspects beyond simple accuracy scores such as efficiency, robustness, fairness, and production readiness.

When to use it

• Comparing different machine learning models (e.g., LLMs) for a specific task.
• Determining which model best fits your particular use case and constraints.
• Designing evaluation protocols to assess predictive performance, computational efficiency, robustness, fairness, calibration, and production readiness.
• Creating test sets including held-out data, edge cases, adversarial examples, and tests for distribution shift.
• Evaluating LLMs based on task accuracy, response quality, safety, alignment, and efficiency.

Key capabilities

• Evaluation Protocol Design: Guides users through defining objectives, selecting metrics (Accuracy, F1, AUC-ROC, RMSE, MAE, R-squared, BLEU, ROUGE, BERTScore, NDCG, MRR, MAP), and planning statistical methodology.
• Comparative Evaluation Execution: Facilitates consistent evaluation across models, confidence interval calculation, and statistical significance testing.
• LLM-Specific Evaluation: Supports defining dimensions like task accuracy (factual, reasoning, coding), response quality, safety, alignment, and efficiency; creating representative datasets with ground truth answers or human preference data; and implementing automated metrics and LLM-as-judge evaluations.
• Metric Selection: Provides a table of common metrics for different task types (Classification, Regression, Ranking, Generation, LLM).

Example prompts

• "Design an evaluation protocol to compare two classification models."
• "What metrics should I use to evaluate the performance of an LLM on a coding task?"
• "Create a test set with adversarial examples for my image classifier."

Tips & gotchas

• The skill emphasizes designing robust evaluation methodologies, so consider all relevant factors beyond just accuracy.
• Proper statistical methodology (sample sizes, confidence intervals, corrections) is crucial for reliable results.
• The skill provides guidance but requires user input and understanding of machine learning principles to effectively implement the evaluation process.