where β0,β1,…,βp are regression coefficients, which are estimated by maximum-likelihood from the considered dataset. The probability that Y=1 for a new instance is then estimated by replacing the β’s by their estimated counterparts and the X’s by their realizations for the considered new instance in Eq. (1). The new instance is then assigned to class Y=1 if P(Y=1)>c, where c is a fixed threshold, and to class Y=0 otherwise. The commonly used threshold c=0.5, which is also used in our study, yields a so-called Bayes classifier. As for all model-based methods, the prediction performance of LR depends on whether the data follow the assumed model. In contrast, the RF method presented in the next section does not rely on any model.
