In this chapter, the authors focus on a method that evaluates the effect of a selected explanatory variable in terms of changes of a model's prediction induced by changes in the variable's values. Ceteris-paribus (CP) profiles show how a model’s prediction would change if the value of a single exploratory variable changed. In essence, a CP profile shows the dependence of the conditional expectation of the dependent variable (response) on the values of the particular explanatory variable. The authors introduce more formally one-dimensional CP profiles. One-dimensional CP profiles, as presented in the chapter, offer a uniform, easy to communicate, and extendable approach to model exploration. However, there are several issues related to the use of the CP profiles. One of the most important ones is related to the presence of correlated explanatory variables. One of the most interesting uses of the CP profiles is the comparison for two or more of models.