Link Function

In statistics and machine learning, a link function is a function that connects a linear predictor to a response variable. It is used in generalized linear models (GLMs) to model the relationship between the response variable and the predictor variables. The link function transforms the expected value of the response variable to a linear combination of the predictor variables. The choice of link function depends on the type of response variable and the distribution of the data. For example, the logit link function is used for binary classification problems, while the identity link function is used for linear regression. The link function is an important component of GLMs as it allows for the modeling of non-linear relationships between the response and predictor variables.
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