Logistic regression makes categorical predictions (true/false, 0 or 1, yes/no), while regular linear regression predicts continuous outcomes (weight, house price).

Logistic regression can be thought of as an extension to, or a special case of, linear regression. If the outcome variable is a continuous variable, linear regression is more suitable.

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems.

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...

Logistic regression. In logistics regression, you can use machine learning to help predict the probability of the outcome of a situation with two potentials.

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Although [Vitor Fróis] is explaining linear regression because it relates to machine learning, the post and, indeed, the topic have wide applications in many things that we do with electronics ...

Logistic regression enables you to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome, or response, can be dichotomous (yes, no) or ordinal (low ...