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evaluating-regression-models

Evaluate regression models with scoring, diagnostics, and cross-validation.

2|Updated May 23, 2026
Search Tags:#diagnostics#regression#time-series#cross-validation#rmse#residuals#mae
One-click install
npx skills add https://github.com/rocklambros/rcs --skill evaluating-regression-models
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-regression-models
Source: https://github.com/rocklambros/rcs/tree/main/skills/ml-datasci/evaluating-regression-models
Command: npx skills add https://github.com/rocklambros/rcs --skill evaluating-regression-models

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill turns raw regression predictions into a defensible evaluation, so you can tell whether a model is actually useful instead of relying on a single headline score.

Core Features & Use Cases

  • Complete scoring: Reports RMSE, MAE, R-squared, and adjusted R-squared when multiple features are involved.
  • Residual diagnostics: Checks residuals versus fitted values, normality with a QQ assessment, and residual shape with a histogram.
  • Validation discipline: Requires cross-validation with mean and spread across folds, and switches to time-series-aware validation when forecasting data is involved.
  • Practical guardrails: Refuses to accept R-squared by itself, flags possible leakage, and highlights influential or high-error rows for review.

Quick Start

Ask Claude to evaluate the regression model by providing y_true, y_pred, the number of features, and any cross-validation or time-series context.

Frequently Asked Questions about evaluating-regression-models

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

FAQPage Schema
How do I evaluate a regression model beyond just looking at R-squared?▼

Evaluating a regression model requires calculating RMSE, MAE, and adjusted R-squared alongside R-squared, plus checking residual plots, QQ assessments, and cross-validation fold stability to ensure the model is actually useful.

What is the best way to validate a time-series forecasting model?▼

Time-series forecasting validation requires time-series-aware cross-validation instead of random folds, reporting the mean and spread across folds to ensure temporal data integrity and stable predictive performance.

How do you perform residual diagnostics for a linear regression model?▼

Residual diagnostics for linear regression involve plotting residuals versus fitted values, assessing normality with a QQ plot, checking residual shape with a histogram, and highlighting influential or high-error rows for review.

Can I compare multiple regression models using cross-validation scores?▼

Comparing regression models requires cross-validation with mean and spread across folds, calculating RMSE and MAE metrics, and flagging possible data leakage to provide a defensible performance certification.

When should I use adjusted R-squared instead of R-squared for model evaluation?▼

Adjusted R-squared is required for model evaluation when multiple features are involved, providing a more accurate scoring metric than bare R-squared claims by penalizing unnecessary predictors in the regression model.

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