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Training a model using a link function is a statistical technique commonly employed in machine learning and data analysis. The primary purpose of utilizing link functions is to establish a connection between the predicted variable’s mean and the linear predictor in a generalized linear model (GLM). This technique plays a crucial role in various modeling scenarios, including logistic regression, Poisson regression, and negative binomial regression, among others.

The link function serves as a bridge between the linear predictor and the mean of the response variable, allowing for the modeling of non-linear relationships. It transforms the linear predictor into a scale that is appropriate for the distribution of the response variable. For instance, in logistic regression, the logit link function is used to model the probability of a binary outcome, while in Poisson regression, the log link function is employed to model the mean of a count variable. By incorporating link functions, GLMs offer greater flexibility and adaptability in modeling diverse types of data and relationships.

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