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ml-evaluation

Compute and summarize machine learning evaluation metrics for classifiers and regressors.

Updated Mar 19, 2026
Search Tags:#metrics#cross-validation#model-evaluation#confusion-matrix#ml-evaluation#roc-auc
One-click install
npx skills add https://github.com/sencersoylu/scholar-flow --skill ml-evaluation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-evaluation
Source: https://github.com/sencersoylu/scholar-flow/tree/main/skills/statistics/ml-evaluation
Command: npx skills add https://github.com/sencersoylu/scholar-flow --skill ml-evaluation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Machine learning evaluation metrics to quantify and interpret model performance across classification and regression tasks.

Core Features & Use Cases

  • Compute standard metrics (accuracy, precision, recall, F1, RMSE, MAE, R^2)
  • Generate ROC-AUC, confusion matrices, and calibration plots
  • Use cases: model validation, comparison, and performance reporting across domains

Quick Start

Apply the evaluation module to a trained model and generate a metrics report.

Frequently Asked Questions about ml-evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute classification metrics like precision, recall, and F1 for a trained model?▼

The module computes standard machine learning evaluation metrics including accuracy, precision, recall, F1, RMSE, MAE, and R^2 to quantify classifier or regressor performance. It applies these metrics during model validation and performance reporting.

How do I generate a confusion matrix and ROC-AUC curve for model evaluation?▼

The module generates ROC-AUC scores, confusion matrices, and calibration plots to visualize and interpret model performance. These outputs support model validation and performance reporting across domains.

What is the best way to compare multiple machine learning models using evaluation metrics?▼

The module computes cross-validation results and standard metrics like RMSE, MAE, and R^2 to support model comparison. It integrates with common ML libraries to calculate scores and generate reporting outputs.

Can I use cross-validation to assess machine learning model performance?▼

The module computes cross-validation results and standard metrics to assess classifier or regressor performance. It integrates with common ML libraries to calculate scores and generate reporting outputs.

What evaluation metrics should I use for regression model validation?▼

For regression model validation, use standard metrics like RMSE, MAE, and R^2. The module computes these scores to quantify and interpret regressor performance during model validation and reporting.

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