Tetrahedron: Barycentric Measure Visualizer
Abstract
Each machine learning task comes equipped with its own set of performance measures. For example, there is a plethora of classification measures that assess predictive performance, a myriad of clustering indices, and equally many rule interestingness measures. Choosing the right measure requires careful thought, as it can influence model selection and thus the performance of the final machine learning system. However, analyzing and understanding measure properties is a difficult task. Here, we present Tetrahedron , a web-based visualization tool that aids the analysis of complete ranges of performance measures based on a two-by-two contingency matrix. The tool operates in a barycentric coordinate system using a 3D tetrahedron, which can be rotated, zoomed, cut, parameterized, and animated. The application is capable of visualizing predefined measures (86 currently), as well as helping prototype new measures by visualizing user-defined formulas.
Cite
Text
Brzezinski et al. "Tetrahedron: Barycentric Measure Visualizer." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71273-4_43Markdown
[Brzezinski et al. "Tetrahedron: Barycentric Measure Visualizer." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/brzezinski2017ecmlpkdd-tetrahedron/) doi:10.1007/978-3-319-71273-4_43BibTeX
@inproceedings{brzezinski2017ecmlpkdd-tetrahedron,
title = {{Tetrahedron: Barycentric Measure Visualizer}},
author = {Brzezinski, Dariusz and Stefanowski, Jerzy and Susmaga, Robert and Szczech, Izabela},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {419-422},
doi = {10.1007/978-3-319-71273-4_43},
url = {https://mlanthology.org/ecmlpkdd/2017/brzezinski2017ecmlpkdd-tetrahedron/}
}