Quantifying probabilistic robustness of tree-based classifiers against natural distortions
This work addresses robustness in trustworthy AI for tree-based models, but it is incremental as it builds on prior methods with specific assumptions.
The paper tackles the problem of quantifying robustness for tree-based classifiers against natural distortions by proposing a method that extracts decision rules and computes exact probabilities of label retention, improving upon an existing approximate measure.
The concept of trustworthy AI has gained widespread attention lately. One of the aspects relevant to trustworthy AI is robustness of ML models. In this study, we show how to probabilistically quantify robustness against naturally occurring distortions of input data for tree-based classifiers under the assumption that the natural distortions can be described by multivariate probability distributions that can be transformed to multivariate normal distributions. The idea is to extract the decision rules of a trained tree-based classifier, separate the feature space into non-overlapping regions and determine the probability that a data sample with distortion returns its predicted label. The approach is based on the recently introduced measure of real-world-robustness, which works for all black box classifiers, but is only an approximation and only works if the input dimension is not too high, whereas our proposed method gives an exact measure.