python-regression-statistics

Provides expert guidance for regression analysis, diagnostics, and outlier detection in Python.

16|Updated Dec 28, 2025

Search Tags:#diagnostics #statistics #regression #scikit-learn #outliers #statsmodels #pyod

Ready to run

npx skills add https://github.com/Hongyu-yu/matsci-ai-skills --skill python-regression-statistics-hongyu-yu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-regression-statistics
Source: https://github.com/Hongyu-yu/matsci-ai-skills/tree/main/skills/python-regression-statistics
Command: npx skills add https://github.com/Hongyu-yu/matsci-ai-skills --skill python-regression-statistics-hongyu-yu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides expert guidance for regression analysis, diagnostics, and outlier detection in Python, enabling principled modeling and robust conclusions.

Core Features & Use Cases

Statistical regression guidance (OLS, WLS, GLM) with interpretation notes and code patterns.
Machine learning regression workflows (Ridge, Lasso, ElasticNet, RandomForest) with cross‑validation and model comparison.
Comprehensive outlier detection strategies (statistical, proximity-based, ensemble) for robust inference.
Use case: A data scientist evaluates multiple regression approaches on a dataset to compare R², RMSE, and diagnostic plots.

Quick Start

Provide a dataset and ask the AI to fit an OLS model, perform diagnostics, and compare ML regression options.

Frequently Asked Questions about python-regression-statistics

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

FAQPage Schema

How do I perform regression diagnostics and outlier detection in Python?▼

Regression diagnostics and outlier detection in Python involve applying statistical tests and proximity-based methods using statsmodels, scikit-learn, and PyOD to evaluate model assumptions and identify anomalous data points. This Skill provides example workflows for robust inference.

What is the best way to compare OLS, Ridge, and Lasso regression models?▼

Comparing OLS, Ridge, and Lasso regression models involves fitting each model and evaluating metrics like R² and RMSE alongside diagnostic plots. This Skill provides cross-validation patterns and model comparison workflows for both statistical and machine learning regression approaches.

Can I use statsmodels for GLM and WLS regression interpretation?▼

Yes, statsmodels supports GLM and WLS regression interpretation. This Skill provides guidance on statistical regression techniques including OLS, WLS, and GLM, offering interpretation notes and code patterns to ensure principled modeling and robust conclusions.

Does this Skill support PyOD for proximity-based outlier detection?▼

Yes, this Skill supports PyOD for outlier detection. It delivers comprehensive outlier detection strategies including statistical, proximity-based, and ensemble methods, integrating PyOD into data science workflows for robust inference and anomaly identification.

How do I run cross-validation patterns for ElasticNet and RandomForest regression?▼

Running cross-validation for ElasticNet and RandomForest regression involves using scikit-learn to partition data and evaluate model performance. This Skill provides machine learning regression workflows with cross-validation patterns to compare multiple regression approaches on a dataset.

When should I use statistical regression instead of machine learning regression?▼

Statistical regression is typically used for inference and interpreting relationships, while machine learning regression focuses on predictive accuracy. This Skill guides when to apply OLS and GLM versus Ridge, Lasso, or RandomForest based on your diagnostic and modeling goals.