One of the most important criteria for selection of the exploration and explanation methods is the number of explanatory variables in the model. In models with a medium or large number of variables, it is still possible that most (or all) of them are interpretable. In models with interactions, the effect of one explanatory variable may depend on values of other variables. For example, the probability of survival for Titanic passengers may decrease with age, but the effect may be different for different travel-classes. Predictive models may use hundreds of explanatory variables to yield a prediction for a particular instance. However, for a meaningful interpretation and illustration, most human beings can handle only a very limited number of variables. The techniques for explaining and exploring models have many applications. The CP-profiles for the random forest-model are, in general, consistent with the logistic regression model. However, for a model with interactions, the additive explanations for individual explanatory variables can be misleading.