Explainable Online Validation of Machine Learning Models for Practical Applications
Abstract
We present a reformulation of the regression and classification, which aims to validate the result of a machine learning algorithm. Our reformulation simplifies the original problem and validates the result of the machine learning algorithm using the training data. Since the validation of machine learning algorithms must always be explainable, we perform our experiments with the kNN algorithm as well as with an algorithm based on conditional probabilities, which is proposed in this work. For the evaluation of our approach, three publicly available data sets were used and three classification and two regression problems were evaluated. The presented algorithm based on conditional probabilities is also online capable and requires only a fraction of memory compared to the kNN algorithm.
Keywords
Cite
@article{arxiv.2010.00821,
title = {Explainable Online Validation of Machine Learning Models for Practical Applications},
author = {Wolfgang Fuhl and Yao Rong and Thomas Motz and Michael Scheidt and Andreas Hartel and Andreas Koch and Enkelejda Kasneci},
journal= {arXiv preprint arXiv:2010.00821},
year = {2021}
}