CLASSify: A Web-Based Tool for Machine Learning
Aaron D. Mullen Samuel E. Armstrong Jeff Talbert V. K. Cody Bumgardner
Machine learning classification problems are widespread in bioinformatics,
but the technical knowledge required to perform model training, optimization,
and inference can prevent researchers from utilizing this technology. This
article presents an automated tool for machine learning classification problems
to simplify the process of training models and producing results while
providing informative visualizations and insights into the data. This tool
supports both binary and multiclass classification problems, and it provides
access to a variety of models and methods. Synthetic data can be generated
within the interface to fill missing values, balance class labels, or generate
entirely new datasets. It also provides support for feature evaluation and
generates explainability scores to indicate which features influence the output
the most. We present CLASSify, an open-source tool for simplifying the user
experience of solving classification problems without the need for knowledge of
machine learning.