Logit Link Function
In statistics and machine learning, a link function is used to connect a linear predictor to a response variable in a generalized linear model. The logit link function is commonly used in binary classification problems, where the response variable is binary (e.g. 0 or 1). The logit function transforms the linear predictor into a probability value between 0 and 1, which represents the probability of the positive class. The logit function is defined as the natural logarithm of the odds ratio, which is the ratio of the probability of the positive class to the probability of the negative class. The logit link function is a monotonic function, which means that as the linear predictor increases, the probability of the positive class also increases. The logit link function is also used in logistic regression, which is a popular method for binary classification.
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