Interpretable Transformed ANOVA Approximation on the Example of the Prevention of Forest Fires - Daniel Potts; Michael Schmischke (Frontiers in Applied Mathematics and Statistics, 2022)
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 L2 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 dataset from the UCI machine learning repository. The attribute ranking obtained from the ANOVA approximation provides us with crucial information about which variables in the dataset are the most important for the detection of fires.