The Experimenter environment is designed to allow the user to create, run, modify and analyze experiments. It is a more convenient way to compare several learning schemes and compare the results to determine if a particular scheme is better than the others. The comparisons can be based on different measures such as accuracy, speed and so on. As such, the Experimenter environment makes it easy to work with several data sets and several learning schemes at once.

A major limitation of the Experimenter environment is that we cannot edit the data set by using filters. Neither is choosing a different class variable or even ignoring any attributes possible. This environment assumes the data to be analyzed has already been cleaned and filtered and that all these operations have already been performed on the data set(s), and that the only thing left is to test classification schemes on it.