Linear regression is quick and dirty and a good way to obtain a baseline for other regression models. It can be trained in sklearn quickly, usually a minute or two for millions of rows.

One of the first steps to optimize linear regression for small datasets is to choose the most relevant features for your model. You want to avoid using too many features that may not have a ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

At its essence, linear regression establishes a relationship between two variables – an independent variable, often referred to as the predictor, and a dependent variable, the outcome. The model ...

This project implements a simple linear regression model using the burn library in Rust. The goal is to generate synthetic data, define a linear regression model, train the model on the synthetic data ...

Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...