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Model Evaluation Metrics

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jeremylongshore·vlatest·ai-ml·MIT

Calculates common machine learning evaluation metrics (accuracy, precision, recall, F1-score) for model performance assessment.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add jeremylongshore/tons-of-skills-marketplace --skill model-evaluation-metrics -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

jeremylongshore

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Install target

Live repository

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Not scanned. none of the 396 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as jeremylongshore/claude-code-plugins-plus-skills.
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About this skill

What it does

This skill allows AI agents to calculate and interpret common model evaluation metrics. It can compute values like accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). The results are presented in a structured format suitable for analysis and comparison of different models or configurations.

When to use it

  • Comparing Model Performance: Evaluate multiple machine learning models trained on the same dataset to determine which performs best.
  • Fine-tuning Models: Assess how changes to model parameters impact key performance indicators.
  • Debugging Model Issues: Identify potential biases or weaknesses in a model by examining metrics across different classes or demographics.
  • Reporting Results: Generate standardized evaluation reports for stakeholders, including clear metric values and interpretations.

Key capabilities

  • Calculates accuracy
  • Calculates precision
  • Calculates recall
  • Calculates F1-score
  • Calculates area under the ROC curve (AUC)

Example prompts

  • "Calculate the accuracy, precision, recall, and F1-score for this dataset: [dataset]"
  • "What is the AUC of this model given these true positives, false positives, true negatives, and false negatives?"
  • "Compare the performance metrics (accuracy, precision, recall) of Model A and Model B based on these results."

Tips & gotchas

The skill requires a structured dataset containing ground truth labels and predicted values. Ensure your data is properly formatted for accurate metric calculation; incorrect formatting can lead to misleading results.