SAS Enterprise Miner Certification Practice Test

In the context of neural networks applied to regression with a binary target variable, the appropriate link function is the logit function. The logit link function transforms the probabilities produced by the neural network into a form that can be modeled more effectively. It does this by taking the natural logarithm of the odds of the target variable being 1 versus it being 0.

Using the logit function is critical in binary classification scenarios because it ensures that the predictions are constrained to the (0, 1) interval, which is necessary for interpreting them as probabilities. This application of the logit function allows for better handling of the non-linear relationship that often exists between the predictors and the binary outcome.

In contrast, the other options do not appropriately suit the requirements of a binary target variable in neural network regression. A logarithmic function, while useful in some contexts, does not specifically address the binary nature of the data. A linear function is not applicable, as it would not constrain the outputs to the 0-1 range necessary for probabilities. Nonlinear is a broad term that does not specify a link function and could include a variety of forms, but it does not inherently imply the appropriate model for a binary outcome. Thus, the logit link function