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 ...

The dependent variable is the variable that is being studied, and it is what the regression model solves for/attempts to predict. In linear regression tasks, every observation/instance is comprised of ...

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 ...

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, ...

In the case of causal methods, the causal model may consist of a linear regression with several explanatory variables. This method is useful when there is no time component. For example ...

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 ...

This is my first PyTorch project—a basic linear regression model built from scratch. As part of my journey to learn PyTorch, I implemented this to understand the fundamentals of machine learning ...