  The distribution of data points is a key component in machine learning. In
most cases, one uses min-max normalization to obtain nodes in $[0,1]$ or
Z-score normalization for standard normal distributed data. In this paper, we
apply transformation ideas in order to design a complete orthonormal system in
the $\mathrm{L}_2$ space of functions with the standard normal distribution as
integration weight. Subsequently, we are able to apply the explainable ANOVA
approximation for this basis and use Z-score transformed data in the method. We
demonstrate the applicability of this procedure on the well-known forest fires
data set from the UCI machine learning repository. The attribute ranking
obtained from the ANOVA approximation provides us with crucial information
about which variables in the data set are the most important for the detection
of fires.
