EDAmame: Interactive exploratory data analyses with explainable models
Date
Authors
Chuah, Aaron
Hewitt, Tim C.
Ali, Sidra A.
May, Maryam
Xu, Tony
Christiadi, Daniel
Choi, Philip Y.I.
Gardiner, Elizabeth E.
Andrews, T. Daniel
Journal Title
Journal ISSN
Volume Title
Publisher
Access Statement
Abstract
Complex tabular datasets comprising many diverse features can require specific expertise to interpret, posing a barrier to researchers with minimal data science experience. EDAmame is an interactive tool that simplifies initial analysis and visualization of these datasets, providing insights into data quality and feature relationships. By leveraging open-source machine learning frameworks in R, EDAmame allows researchers to perform effective exploratory data analysis without command-line or coding requirements.
Description
Keywords
Citation
Collections
Source
Bioinformatics
Type
Book Title
Entity type
Publication
Access Statement
License Rights
Restricted until
Downloads
File
Description