Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Landscape of R packages for eXplainable Artificial Intelligence
33
Zitationen
3
Autoren
2020
Jahr
Abstract
The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need methods for exploration, debugging, and validation. New methods and tools for this purpose are being developed within the eXplainable Artificial Intelligence (XAI) subdomain of machine learning. In this work (1) we present the taxonomy of methods for model explanations, (2) we identify and compare 27 packages available in R to perform XAI analysis, (3) we present an example of an application of particular packages, (4) we acknowledge recent trends in XAI. The article is primarily devoted to the tools available in R, but since it is easy to integrate the Python code, we will also show examples for the most popular libraries from Python.
Ähnliche Arbeiten
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
2017 · 21.050 Zit.
Generative Adversarial Nets
2023 · 19.896 Zit.
Visualizing and Understanding Convolutional Networks
2014 · 15.381 Zit.
"Why Should I Trust You?"
2016 · 14.789 Zit.
Generative adversarial networks
2020 · 13.381 Zit.