The success of artificial intelligence (AI), and deep learning models in particular, has led to
their widespread adoption across various industries due to their ability to process huge …

Abstract Gradient Boost Decision Trees (GBDT) is a powerful additive model based on tree
ensembles. Its nature makes GBDT a black-box model even though there are multiple …

The use of Shapley Values (SVs) to explain machine learning model predictions is
established. Recent research efforts have been devoted to generating efficient Neural …

As machine learning models are being extensively deployed across many applications,
concerns are rising with regard to their trustability. Explainable models have become an …

Black box machine learning models are currently being used for high-stakes decision
making in various parts of society such as healthcare and criminal justice. While tree-based …

In this paper, we examine the bias towards high-entropy features exhibited by SHAP values
on tree-based structures such as classification and regression trees, random forests or …

Environmental, social and governance (ESG) considerations play an increasingly important
role due to the growing focus on sustainability globally. Entities, such as banks and …

Researchers using machine learning methods for classification can face challenges due to
class imbalance, where a certain class is underrepresented. Over or under-sampling of …

The paper focuses on a medical diagnostic procedure supported by decision models
generated by suitable tree-based machine learning algorithms like C4. 5. The typical result …

The goal of a new field of research called Explainable AI (XAI) is to make machine learn ing
algorithms more transparent and comprehensible. This abstract looks at how XAI ideas are …