Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

While deep learning models have achieved state-of-the-art accuracies for many prediction
tasks, understanding these models remains a challenge. Despite the recent interest in …

Understanding predictive models, in terms of interpreting and identifying actionable insights,
is a challenging task. Often the importance of a feature in a model is only a rough estimate …

With the growing adoption of machine learning techniques, there is a surge of research
interest towards making machine learning systems more transparent and interpretable …

Massive simulations and arrays of sensing devices, in combination with increasing
computing resources, have generated large, complex, high-dimensional datasets used to …

Visual analytics systems combine machine learning or other analytic techniques with
interactive data visualization to promote sensemaking and analytical reasoning. It is through …

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image
classification. With a growing number of classes, the accuracy usually drops as the …

Performance analysis is critical in applied machine learning because it influences the
models practitioners produce. Current performance analysis tools suffer from issues …

As an ensemble model that consists of many independent decision trees, random forests
generate predictions by feeding the input to internal trees and summarizing their outputs …