Regression model - Tutorial

This resource offers an in-depth exploration of various regression models commonly used in statistical analysis and machine learning. It covers a wide range of techniques including linear regression, polynomial regression, ridge regression, lasso regression, elastic net, decision tree regression, random forest regression, support vector regression, and others. For each model, it details the fundamental characteristics that define how the model operates, the underlying mathematical or statistical assumptions required for accurate performance, and a thorough explanation of the hyperparameters involved. These hyperparameters — such as learning rate, regularization strength, number of estimators, kernel type, and maximum tree depth — play a crucial role in controlling model complexity, preventing overfitting, and improving predictive accuracy. Understanding these aspects helps practitioners choose the most appropriate model for a given dataset and fine-tune it effectively to achieve optimal results.