This book is about making machine learning models and their decisions interpretable. After
exploring the concepts of interpretability, you will learn about simple, interpretable models …

We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …

Counterfactual explanations are one of the most popular methods to make predictions of
black box machine learning models interpretable by providing explanations in the form of …

Abstract Explainable Artificial Intelligence (xAI) is an established field with a vibrant
community that has developed a variety of very successful approaches to explain and …

Complex, non-parametric models, which are typically used in machine learning, have
proven to be successful in many prediction tasks. But these models usually operate as black …

Objectives To analyse the impact of tumour necrosis factor inhibitors (TNFis) on spinal
radiographic progression in ankylosing spondylitis (AS). Methods Patients with AS in the …

In recent years, a large amount of model-agnostic methods to improve the transparency,
trustability, and interpretability of machine learning models have been developed. Based on …

An increasing number of model-agnostic interpretation techniques for machine learning
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …

The interpretation of feature importance in machine learning models is challenging when
features are dependent. Permutation feature importance (PFI) ignores such dependencies …

Scientists and practitioners increasingly rely on machine learning to model data and draw
conclusions. Compared to statistical modeling approaches, machine learning makes fewer …