What is a Generalized Linear Model (GLM)?
The concept of Generalized Linear Model (GLM) extends the framework developed in linear regression to outcomes that are not normally distributed, such as binary, count, or proportion data.
The concept of Generalized Linear Model (GLM) extends the framework developed in linear regression to outcomes that are not normally distributed, such as binary, count, or proportion data.
Gamma Regression: The gamma distribution is used to model non-negative data that has an inherent right skew, such as income.
Poisson regression is used where target variable is measured in counts.
Poisson regression uses the following cost function:
The poisson distribution is specified by one parameter lambda that represents both the mean and variance of the distribution.
In many data generation processes for count data, it is possible that a lot of observations will have a count of zero.
The gamma distribution is used to model non-negative data that has an inherent right skew, such as income.
The beta distribution is used to model proportion data, as its support is limited to the range between 0 and 1.
The tweedie distribution has a density that follows an exponential curve but has a large concentration of data points around 0.
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