Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision data

arXiv:2404.09541v1 Announce Type: new
Abstract: Machine learning algorithms are fundamental components of novel data-informed Artificial Intelligence architecture. In this domain, the imperative role of representative datasets is a cornerstone in shaping the trajectory of artificial intelligence (AI) development. Representative datasets are needed to train machine learning components properly. Proper training has multiple impacts: it reduces the final model's complexity, power, and uncertainties. In this paper, we investigate the reliability of the $\varepsilon$-representativeness method to assess the dataset similarity from a …

abstract algorithms application architecture artificial artificial intelligence arxiv collision components cs.lg data datasets decision decision trees development domain intelligence machine machine learning machine learning algorithms novel reliability role trajectory trees type