Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-71273-4_43 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Data mining,Parameterized complexity,Matrix (mathematics),Visualization,Computer science,Model selection,Cluster analysis,Tetrahedron,Contingency,Barycentric coordinate system | Conference | 10536 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dariusz Brzezinski | 1 | 213 | 11.28 |
| Jerzy Stefanowski | 2 | 1653 | 139.25 |
| Robert Susmaga | 3 | 370 | 33.32 |
| Izabela Szczęch | 4 | 56 | 7.90 |