Uses a FeedForwardNetwork to solve binary classification problems. In these problems, the target output for the network is the probability that the pattern falls into one of two classes. The first class, , is usually equal to one and the second class, equal to zero. These probabilities are then used to assign patterns to one of the two classes. Typical applications include determining whether a credit applicant is a good or bad credit risk, and determining whether a person should or should not receive a particular treatment based upon their physical, clinical and laboratory information. This class signals that network training will minimize the binary cross-entropy error, and that network output is the probability that the pattern belongs to the first class, . Which is calculated by applying the logistic activation function to the potential of the single output. The probability for the second class is calculated by .