Imported from jeremylongshore/tons-of-skills-marketplace (plugins/ai-ml/model-evaluation-suite/skills/evaluating-machine-learning-models/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill evaluating-machine-learning-models. Copyright stays with the author (MIT).

Model Evaluation Suite

Evaluate machine learning models using a comprehensive suite of metrics including accuracy, precision, recall, F1-score, and custom KPIs.

Overview

This skill empowers Claude to perform thorough evaluations of machine learning models, providing detailed performance insights. It leverages the model-evaluation-suite plugin to generate a range of metrics, enabling informed decisions about model selection and optimization.

How It Works

1. Analyzing Context: Claude analyzes the user's request to identify the model to be evaluated and any specific metrics of interest.
2. Executing Evaluation: Claude uses the /eval-model command to initiate the model evaluation process within the model-evaluation-suite plugin.
3. Presenting Results: Claude presents the generated metrics and insights to the user, highlighting key performance indicators and potential areas for improvement.

When to Use This Skill

This skill activates when you need to:

• Assess the performance of a machine learning model.
• Compare the performance of multiple models.
• Identify areas where a model can be improved.
• Validate a model's performance before deployment.

Examples

Example 1: Evaluating Model Accuracy

User request: "Evaluate the accuracy of my image classification model."

The skill will:

1. Invoke the /eval-model command.
2. Analyze the model's performance on a held-out dataset.
3. Report the accuracy score and other relevant metrics.

Example 2: Comparing Model Performance

User request: "Compare the F1-score of model A and model B."

The skill will:

1. Invoke the /eval-model command for both models.
2. Extract the F1-score from the evaluation results.
3. Present a comparison of the F1-scores for model A and model B.

Best Practices

• Specify Metrics: Clearly define the specific metrics of interest for the evaluation.
• Data Validation: Ensure the data used for evaluation is representative of the real-world data the model will encounter.
• Interpret Results: Provide context and interpretation of the evaluation results to facilitate informed decision-making.

Integration

This skill integrates seamlessly with the model-evaluation-suite plugin, providing a comprehensive solution for model evaluation within the Claude Code environment. It can be combined with other skills to build automated machine learning workflows.

Prerequisites

• Appropriate file access permissions
• Required dependencies installed

Instructions

1. Invoke this skill when the trigger conditions are met
2. Provide necessary context and parameters
3. Review the generated output
4. Apply modifications as needed

Output

The skill produces structured output relevant to the task.

Error Handling

• Invalid input: Prompts for correction
• Missing dependencies: Lists required components
• Permission errors: Suggests remediation steps

Resources

• Project documentation
• Related skills and commands