What libraries does the ROC Curve Plotter use?

The skill primarily generates code utilizing Scikit-learn for metric calculation and Matplotlib or Seaborn for high-quality visual plotting.

Is the generated code compatible with Jupyter Notebooks?

Absolutely. The skill generates standard Python code that works seamlessly in scripts, Jupyter Notebooks, and integrated ML pipelines.

Does it calculate the AUC score automatically?

Yes, it generates the necessary Python logic to calculate both the visual ROC curve and the precise Area Under the Curve (AUC) numerical value.

Can I use this for multi-class classification?

Yes, the skill provides implementation patterns for multi-class problems using One-vs-Rest (OvR) and One-vs-One (OvO) strategies.

How do I trigger this skill in Claude Code?

The skill activates automatically when you mention 'roc curve plotter' or ask for help visualizing classification model performance.

ROC Curve Plotter

Name: ROC Curve Plotter
Author: jeremylongshore

byjeremylongshore

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1,031

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데이터 과학 및 ML

Generates professional ROC curves and calculates AUC metrics to evaluate machine learning classification model performance.

The ROC Curve Plotter skill is a specialized tool for Claude Code designed to streamline the evaluation of machine learning models. It automates the generation of Receiver Operating Characteristic (ROC) curves and calculates the Area Under the Curve (AUC), providing data scientists with immediate visual feedback on classifier performance. By leveraging standard Python libraries like Scikit-learn and Matplotlib, this skill helps users compare multiple models, determine optimal classification thresholds, and ensure best practices in model validation directly within their development environment.

주요 기능

01Automated ROC curve visualization using Scikit-learn and Matplotlib

02Implementation patterns for multi-class ROC analysis

03Precise Area Under the Curve (AUC) metric calculation

04Production-ready Python code generation for model evaluation

051,031 GitHub stars

06Support for comparing multiple classifiers on a single plot

사용 사례

01Visualizing the trade-off between sensitivity and specificity in binary classifiers

02Documenting model performance for machine learning experiments and reports

03Comparing the performance of different model architectures (e.g., Random Forest vs. XGBoost)

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills roc-curve-plotter

For use in Claude.ai and ChatGPT

Download Skill