Conceptos Básicos
The authors explore visual methods for analyzing probabilistic classification data, focusing on the structure of large-scale classifiers and the interpretation of confusion matrices.
Resumen
This content delves into various studies related to visualizing classification structures, interpreting confusion matrices, and analyzing machine learning models. Authors investigate methods for understanding complex data relationships in machine learning through visualization techniques.
Estadísticas
Alsallakh et al . (2014) Bilal Alsallakh, Allan Hanbury, Helwig Hauser, Silvia Miksch, and Andreas Rauber. 2014. Visual methods for analyzing probabilistic classification data.
Alsallakh et al . (2017) Bilal Alsallakh, Amin Jourabloo, Mao Ye, Xiaoming Liu, and Liu Ren. 2017. Do convolutional neural networks learn class hierarchy?
Hinterreiter et al . (2020) A. Hinterreiter, P. Ruch, H. Stitz, M. Ennemoser, J. Bernard, H. Strobelt, and M. Streit. 2020. ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion.
Krstinić et al . (2020) Damir Krstinić, Maja Braović, Ljiljana Šerić, and Dunja Božić-Štulić. 2020. Multi-label classifier performance evaluation with confusion matrix.
Shen et al . (2020) Hong Shen, Haojian Jin, Ángel Alexander Cabrera, Adam Perer, Haiyi Zhu, and Jason I Hong. 2020. Designing alternative representations of confusion matrices to support non-expert public understanding of algorithm performance.