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Performance evaluation for learning systems

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If you have a question about this talk, please contact Pietro Lio .

In the talk we review the need to revisit performance evaluation in Machine Learning, as long as the existing mainstream options (accuracy, f-measure, MSE /MAE) provide a too narrow insight on method performance. Some of the topics discussed in the talk relate to the interpretation and modelling of data in a dataset, including multiple ground truth, classes of equivalence, and area-based interpretation of input population. A later part of the talk reviews alternatives to accuracy and f-measure in literature, mostly leaning towards inclusion of explainability or truthworthiness.

This talk is part of the Foundation AI series.

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