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validation

Compute ML metrics, cross-validate, and analyze residuals.

Updated Feb 7, 2026
Search Tags:#metrics#cross-validation#model-evaluation#ml-pipeline#shap#feature-importance#residual-analysis
One-click install
npx skills add https://github.com/root-5/agentic-ml-pipeline --skill validation-root-5
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: validation
Source: https://github.com/root-5/agentic-ml-pipeline/tree/main/skills/validation
Command: npx skills add https://github.com/root-5/agentic-ml-pipeline --skill validation-root-5

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Evaluate machine learning model performance and generalization across datasets and tasks, providing standardized evaluation workflows.

Core Features & Use Cases

  • Compute common metrics such as accuracy, precision, recall, F1-score, RMSE, AUC, and LogLoss.
  • Perform cross-validation and error analysis to assess stability and generalization.
  • Inspect feature importance and residuals to diagnose model behavior and improvement opportunities.
  • Use case: Validate a trained classifier on held-out data to compare model variants and report results.

Quick Start

Run the evaluation workflow on your latest model to generate validation metrics and reports.

Frequently Asked Questions about validation

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

FAQPage Schema
How do I evaluate machine learning model performance and generalization across datasets?▼

To evaluate machine learning model performance, you can compute common metrics like accuracy, precision, recall, F1-score, RMSE, AUC, and LogLoss. This workflow provides standardized evaluation across datasets to assess stability and generalization.

How do I perform cross-validation and residual analysis on a trained classifier?▼

Cross-validation and residual analysis are performed to assess model stability and diagnose behavior. The workflow computes validation metrics and generates reports on held-out data to compare model variants.

What is the best way to compute ML metrics and inspect feature importance for model evaluation?▼

The best way to compute ML metrics for model evaluation is to run a standardized workflow that calculates classification and regression metrics while inspecting feature importance to diagnose model behavior and identify improvement opportunities.

Can I export model evaluation results to notebooks and logs for comparison?▼

Yes, you can export model evaluation results to notebooks and logs. The workflow supports computing validation metrics, generating reports, and exporting outputs to document model variant comparisons.

When do I need to run a cross-validation and error analysis workflow on my ML pipeline?▼

You need to run cross-validation and error analysis on your ML pipeline when you want to assess model stability and generalization. It helps diagnose model behavior and identify improvement opportunities across different datasets.

Does this model evaluation workflow support both classification and regression metrics?▼

Yes, the model evaluation workflow supports both classification and regression metrics. It computes common metrics such as accuracy, precision, recall, F1-score, RMSE, AUC, and LogLoss to evaluate performance and generalization.

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