python-regression-statistics

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.

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.

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.

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.

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.

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.