Agent skill

Regression Modeling

Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification

majiayu000534★ · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill regression-modeling --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/analysis/regression-modeling/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Regression Modeling ## Overview Regression modeling predicts continuous target values based on input features, establishing quantitative relationships between variables for forecasting and analysis. ## When to Use - Predicting sales, prices, or other continuous numerical outcomes - Understanding relationships between independent and dependent variables - Forecasting trends based on historical data - Quantifying the impact of features on a target variable - Building baseline models for comparison with more complex algorithms - Identifying which variables most influence predictions ## Regression Types - **Linear Regression**: Straight-line fit to data - **Polynomial Regression**: Non-linear relationships - **Ridge (L2)**: Regularization to prevent overfitting - **Lasso (L1)**: Feature selection through regularization - **ElasticNet**: Combines Ridge and Lasso - **Robust Regression**: Resistant to outliers ## Key Metrics - **R² Score**: Proportion of variance explained - **RMSE**: Root Mean Squared Error - **MAE**: Mean Absolute Error - **AIC/BIC**: Model comparison criteria ## Implementation with Python ```python import pandas as pd import numpy as np import matplotlib.pyplot as pl

What's inside
Steps it walks through
  1. Overview
  2. When to Use
  3. Regression Types
  4. Key Metrics
  5. Implementation with Python
  6. Assumption Checking
  7. Model Selection
  8. Deliverables
Ships with 1 file
  • metadata.json
More from claude-skill-registry
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About this skill
What does the Regression Modeling skill do?

Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill regression-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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