What is it about?

In a classification task, class labels for unknown data points have to be predicted based on known examples. However, for many applications, not only the predicted labels but also their predicted probability is of interest. This probability can be understood as a measure of confidence: the probability of each class label indicates the certainty of the label being the correct prediction. Classification models that provide reliable probability estimates are called well-calibrated. We propose a novel classification approach that is particularly designed to provide well-calibrated models. Our approach is based on a geometric latent space that is induced by a regular simplex with a dimension that depends on the number of classes. The visualization of the latent spaces allows us to get additional insights into the model behavior and structure of the training data.

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Why is it important?

In many real-world scenarios, well-calibrated classifiers are crucial. For example, consider that in a clinical application, a machine learning model is used to predict a diagnosis from patient data. Then it becomes clear that for reliable use of such a model, it is important to know how certain a diagnosis prediction is. In particular, there may be cases in which different diagnoses apply with a similar probability.


A simplex approach to calibrated models is not only a very versatile mathematical tool, but also allows an intuitive interpretation of the model behavior.

Raoul Heese
Fraunhofer-Gesellschaft zur Forderung der angewandten Forschung eV

In addition to its conceptual simplicity and intuitiveness, the proposed simplex-mapping classifier is promising also from a computational point of view.

Dr. Jochen Schmid
Fraunhofer-Gesellschaft zur Forderung der angewandten Forschung eV

Read the Original

This page is a summary of: Calibrated simplex-mapping classification, PLoS ONE, January 2023, PLOS,
DOI: 10.1371/journal.pone.0279876.
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