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@TECHREPORT{Morais_Idiap-Com-01-2023,
         author = {Morais, Antonio},
         editor = {Anjos, Andr{\'{e}} and Odobez, Jean-Marc},
       projects = {Idiap},
          month = {2},
          title = {A Bayesian approach to machine learning model comparison},
           type = {Idiap-Com},
         number = {Idiap-Com-01-2023},
           year = {2023},
    institution = {Idiap},
         school = {EPFL},
       abstract = {Performance measures are an important component of machine learning algorithms. They are useful when it comes to evaluate the quality of a model, but also to help the algorithm improve itself. Every need has its own metric. However, when we have a small data set, these measures don’t express properly the performance of the model. That’s when confidence intervals and credible regions come in handy. Expressing the performance measures in a probabilistic setting lets us develop them as distributions. Then we can use those distributions to establish credible regions. In the first instance we will address the precision, recall and F1-score followed by the accuracy, specificity and Jaccard index. We will study the coverage of the credible regions computed through the posterior distributions. Then we will discuss ROC curve, precision-recall curve and k-fold cross-validation. Finally we will conclude with a small discussion about what we could do with dependent samples.},
            pdf = {https://publications.idiap.ch/attachments/reports/2022/Morais_Idiap-Com-01-2023.pdf}
}