Machine learning methods are widely used within the medical field. However, the reliability
and efficacy of these models is difficult to assess, making it difficult for researchers to identify …

Many important questions about a model cannot be answered just by explaining how much
each feature contributes to its output. To answer a broader set of questions, we generalize a …

Predictive models used in Decision Support Systems (DSS) are often requested to explain
the reasoning to users. Explanations of instances consist of two parts; the predicted label …

Abstract Background and Aims All fields have seen an increase in machine‐learning
techniques. To accurately evaluate the efficacy of novel modeling methods, it is necessary to …

In recent years, the use of sophisticated statistical models that influence decisions in
domains of high societal relevance is on the rise. Although these models can often bring …

Local model-agnostic additive explanation techniques decompose the predicted output of a
black-box model into additive feature importance scores. Questions have been raised about …

We propose a novel method for explaining the predictions of any classifier. In our approach,
local explanations are expected to explain both the outcome of a prediction and how that …

A feature-based model explanation denotes how much each input feature contributes to a
model's output for a given data point. As the number of proposed explanation functions …

Nowadays, the interpretation of why a machine learning (ML) model makes certain
inferences is as crucial as the accuracy of such inferences. Some ML models like the …

Abstract Machine learning methods are widely used within the medical field to enhance
prediction. However, little is known about the reliability and efficacy of these models to …