2017

rozdział w monografii naukowej / referat

angielski

EN 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.

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10.1007/978-3-319-71273-4_43

https://link.springer.com/chapter/10.1007/978-3-319-71273-4_43

Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III

Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2017, 18-22.09.2017, Skopje, Macedonia, the former Yugoslav Republic of

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WoS (15)