## What are linear models supported by GLM?

The models include Linear Regression, Logistic Regression, and Poisson Regression. In a Linear Regression Model, the response (aka dependent/target) variable ‘y’ is expressed as a linear function/linear combination of all the predictors ‘X’ (aka independent/regression/explanatory/observed variables).

### What is the difference between generalized linear model and general linear model?

The general linear model requires that the response variable follows the normal distribution whilst the generalized linear model is an extension of the general linear model that allows the specification of models whose response variable follows different distributions.

**What does a GLM do?**

The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.

**What is a Generalised linear model for dummies?**

Generalized linear models are a group of models with some common attributes. These common attributes are: The distribution of the response variable (i.e. the label), given an input x, is a member of the exponential family of distributions.

## Why is generalized linear models important?

GLM are very important for biomedical applications since they include logistic and Poisson regression, which are often used in biomedical science to model binary outcomes or counts data, respectively.

### Is GLM a linear model?

The term general linear model (GLM) usually refers to conventional linear regression models for a continuous response variable given continuous and/or categorical predictors. The first widely used software package for fitting these models was called GLIM.

**Is GLM supervised learning?**

Today’s topic is Generalized Linear Models, a bunch of general machine learning models for supervised learning problems(both for regression and classification). Let’s start with linear regression models.

0