Last updated: 2023-01-28
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Knit directory: multiclass_AUC/
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| html | b13919c | Ross Gayler | 2023-01-26 | Add notebook 04_score_dependencies |
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| html | 0cdb34a | Ross Gayler | 2023-01-24 | Initial commit |
This is an open, shareable, reproducible, computational research project on using multiclass AUC as a performance metric for multiclass classification.
It is the work of Ross W. Gayler
It is a common data science task to classify inputs by assigning each input to one of a fixed number of categories. Given the existence of a set of inputs with known categories we want to apply the classification model and assess its performance by comparing the assigned categories to the known categories.
Accuracy is a commonly used performance metric. However, accuracy assumes that all misclassifications are equally costly and that the base rates of the categories are fixed. Signal Detection Theory (SDT) explicitly addresses these assumptions and Area Under the Curve (AUC) is a common SDT metric of discriminability. AUC is most commonly dealt with in the context of binary classification. AUC for multiclass classification is much less commonly encountered in practice and there is no commonly accepted agreement on how best to apply it
The objective of this project is to apply multiclass AUC approaches to some trial datasets to get a better understanding of how to apply it and the advantages/disadvantages.
The point of making it an open, shareable, reproducible project is that anyone should be able to copy it, reproduce the analyses, and try out modifications.