Research paper review: From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions

I have decided to read a research paper to see if I could glean any new relevant information regarding machine learning, so happened across the paper, From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions, written by Johannes Resin, and can be read here:- https://www.jmlr.org/papers/volume24/23-0106/23-0106.pdf

This was a very interesting paper, which went into a great deal of technical detail. The abstract of the paper discusses the Brier score as a metric to use in calculating the accuracy of a classification model, and it was a relatively easy matter for me to research the it and even write my own blog post on the subject, which can be read here:- https://medium.com/ai-mind-labs/measure-the-accuracy-of-probabilistic-predictions-using-the-brier-score-c67c5ebeeaa5

The Brier score is a commonly used evaluation metric in machine learning for assessing the quality of probabilistic predictions made by models, especially in binary classification tasks. In binary classification, the Brier score measures the discrepancy between the predicted probabilities and the corresponding binary true labels. It quantifies how well the model’s predicted probabilities align…