RapidMiner (formerly YALE) supports flexible (re)arrangements of the data mining process, which allows the search for the best learning scheme and preprocessing for the data and learning task at hand. The simple adaptation and evaluation of different process designs allow the comparison of different solutions.

Due to the modular operator concept often only one operator has to be replaced to evaluate its performance while the rest of the data mining process design remains the same. This is an important feature for the optimization of real-world data mining processes.

To guide the transformation of the feature space or the automatic search for the best preprocessing, the user can define additional meta data on the data set at hand. Meta data include the type of attributes or the information if the values are ordered. This information is for example used by the feature generation / construction algorithms provided by RapidMiner. The definition of meta information on your data is optional and, if it is omitted, RapidMiner tries to guess the correct data types.