  A key task of data science is to identify relevant features linked to certain
output variables that are supposed to be modeled or predicted. To obtain a
small but meaningful model, it is important to find stochastically independent
variables capturing all the information necessary to model or predict the
output variables sufficiently. Therefore, we introduce in this work a framework
to detect linear and non-linear dependencies between different features. As we
will show, features that are actually functions of other features do not
represent further information. Consequently, a model reduction neglecting such
features conserves the relevant information, reduces noise and thus improves
the quality of the model. Furthermore, a smaller model makes it easier to adopt
a model of a given system. In addition, the approach structures dependencies
within all the considered features. This provides advantages for classical
modeling starting from regression ranging to differential equations and for
machine learning.
  To show the generality and applicability of the presented framework 2154
features of a data center are measured and a model for classification for
faulty and non-faulty states of the data center is set up. This number of
features is automatically reduced by the framework to 161 features. The
prediction accuracy for the reduced model even improves compared to the model
trained on the total number of features. A second example is the analysis of a
gene expression data set where from 9513 genes 9 genes are extracted from whose
expression levels two cell clusters of macrophages can be distinguished.
