  Classification is a vital tool that is important for modelling many complex
numerical models. A model or system may be such that, for certain areas of
input space, the output either does not exist, or is not in a quantifiable
form. Here, we present a new method for classification where the model outputs
are given distinct classifying labels, which we model using a latent Gaussian
process (GP). The latent variable is estimated using MCMC sampling, a unique
likelihood and distinct prior specifications. Our classifier is then verified
by calculating a misclassification rate across the input space.
  Comparisons are made with other existing classification methods including
logistic regression, which models the probability of being classified into one
of two regions. To make classification predictions we draw from an independent
Bernoulli distribution, meaning that distance correlation is lost from the
independent draws and so can result in many misclassifications. By modelling
the labels using a latent GP, this problem does not occur in our method. We
apply our novel method to a range of examples including a motivating example
which models the hormones associated with the reproductive system in mammals,
where the two labelled outputs are high and low rates of reproduction.
