Cybercriminals and cybersecurity are a cat-and-mouse pair. Only that at different times they keep swapping their roles. This ...

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predictions. Common CE ...

In the subject of machine learning, it is essential to comprehend regression algorithms. Ten fundamental regression algorithms are introduced in this tutorial, which serves as the foundation for many ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

Abstract: Conditional quantiles obtained via regression are used as a robust alternative to classical conditional means in econometrics and statistics, as they can capture the uncertainty in a ...