ABSTRACT
In a broad sense, predictive models describe the functional relationship between input and output variables of a data set. When dealing with real-world manufacturing applications, it is usually not an easy task to precisely define the set of input variables that potentially affect the output variables for a particular process. Oftentimes, this is further complicated by the existence of interactions between the variables. Even if these variables can be identified, finding an analytical expression of the relationship may not always be possible. The process of selecting the analytical expression and estimating the parameters of the selected expression could be very time-consuming.
