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Aster and Generalized Linear Model Functionality

Summary:  The generalized linear model (GLM) extends from the general linear model to accommodate dependent variables that are not normally distributed.  GLM is a methodology for modeling relationships between variables.

Use Cases:  

  -  Insurance and Loss Prediction

 -  Fraud Detection/Payment Default/Mortgage Default

  -  Medical Disease Prediction

Simple Explanation of GLM:

GLM Has Three Parts:

1.Response Component – Random or Dependent Variable

2.Predictor Component – Independent Variables

3.Link Function – A function that links predictor components to predicted mean of the response.  Defines the noise or error around the response mean.

Common Family Functions:

GLM Mainly Supports Three Families:

1.Binomial or Linear Regression – Dependent Variable only has 2 possible values (T/F or 1/0)

2.Poisson Regression – Independent events that occur over a Time series/Space

3.Gaussian Regression – Data is grouped around a single mean (Bell Curve Distribution)

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