Given thousands of equally accurate machine learning (ML) models, how can users choose
among them? A recent ML technique enables domain experts and data scientists to …

Bagging and boosting are two popular ensemble methods in machine learning (ML) that
produce many individual decision trees. Due to the inherent ensemble characteristic of …

Dimensionality reduction techniques are widely used for visualizing high-dimensional data.
However, support for interpreting patterns of dimension reduction results in the context of the …

As the complexity of machine learning (ML) models increases and their application in
different (and critical) domains grows, there is a strong demand for more interpretable and …

Deep learning (DL) models have achieved state-of-the-art performance in many domains.
The interpretation of their working mechanisms and decision-making process is essential …

The high performance of tree ensemble classifiers benefits from a large set of rules, which,
in turn, makes the models hard to understand. To improve interpretability, existing methods …

Dimensionality reduction techniques are widely used for visualizing high-dimensional data.
However, support for interpreting patterns of dimension reduction results in the context of the …

Explainable AI (XAI) has become increasingly important in the fast-evolving field of AI and
ML. The complexity and obscurity of AI, especially in the context of deep learning, provide …

Artificial intelligence (AI) systems have been constructed as black boxes that cover their
internal logic and learning approach from humans, and this has led to several unanswered …

The dry bean dataset of this study uses the imbalanced data set of the University of
California, Irvine (UCI) machine learning warehouse platform. The dataset consists of …