Version 1
: Received: 13 February 2024 / Approved: 14 February 2024 / Online: 14 February 2024 (10:56:50 CET)
How to cite:
Bi, Z.; Gao, R.; Fang, S. A General Framework for Visualizing Machine Learning Models. Preprints2024, 2024020798. https://doi.org/10.20944/preprints202402.0798.v1
Bi, Z.; Gao, R.; Fang, S. A General Framework for Visualizing Machine Learning Models. Preprints 2024, 2024020798. https://doi.org/10.20944/preprints202402.0798.v1
Bi, Z.; Gao, R.; Fang, S. A General Framework for Visualizing Machine Learning Models. Preprints2024, 2024020798. https://doi.org/10.20944/preprints202402.0798.v1
APA Style
Bi, Z., Gao, R., & Fang, S. (2024). A General Framework for Visualizing Machine Learning Models. Preprints. https://doi.org/10.20944/preprints202402.0798.v1
Chicago/Turabian Style
Bi, Z., Raymond Gao and Shiaofen Fang. 2024 "A General Framework for Visualizing Machine Learning Models" Preprints. https://doi.org/10.20944/preprints202402.0798.v1
Abstract
As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.
Keywords
machine learning; multi-dimensional data visualization; classification; morphing; scattered data interpolation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
