Profile data is a particular type of data that can stem from a number of different structures. This type of data can occur if a sample is measured repeatedly over time, if a sample has many highly related/correlated predictors, or if sample measurements occur through a hierarchical structure. Whatever the case, the analyst needs to be keenly aware of what the experimental unit is. Understanding the unit informs decisions about how the profiles should be preprocessed, how samples should be allocated to training and test sets, and how samples should be allocated during resampling.
Basic preprocessing steps for profiled data can include reducing baseline effect, reducing noise across the profile, and harnessing the information contained in the correlation among predictors. An underlying goal of these steps is to remove the characteristics that prevent this type of data from being used with most predictive models while simultaneously preserving the predictive signal between the profiles and the outcome. No one particular combination of steps will work for all data. However, putting the right combination of steps together can produce a very effective model.