# Interpretable transformed ANOVA approximation on the example of the prevention of forest fires

arXiv: https://arxiv.org/abs/2110.07353 · HTML: https://mlsanity.com/paper/2110.07353/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 1 of 1)

- [Interpretable Approximation of High-Dimensional Data](https://mlsanity.com/paper/2103.13787/) (2021)

## Built on by (most cited first, 1 of 1)

- [Data Compression using Rank-1 Lattices for Parameter Estimation in Machine Learning](https://mlsanity.com/paper/2409.13453/) (2024)

## Abstract (arXiv)

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 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.