Measuring Calibration in Deep Learning
Overconfidence and underconfidence in machine learning classifiers is measured by calibration: the degree to which the probabilities predicted for each class match the accuracy of the classifier on that prediction. We propose two new measures for calibration, the Static Calibration Error (SCE) and Adaptive Calibration Error (ACE). These measures take into account every prediction made by a model, in contrast to the popular Expected Calibration Error.
After an interesting review of calibration methods, the paper describes two new methods for assessing calibration. The first method, SCE, is an extension of the usual ECE to the multi class setting. The second method, ACE, is a slight variation where bins are computed adaptively. The paper is interesting and relatively well written. Although the contribution is rather simple (can be describe in less than a page), I can see myself using the SCE/ACE metrics in the near future. In order to strengthen the submission, I still feel that the authors should try to describe why the newly introduced metrics can help in applied scenarios. (1) OK, the ECE omits a lot of important predictions (e.g. fig 1) --> give a real application where this matters (2) OK, adaptive binning seems a sensible approach. --> give a real application where a difference between 0.99 and 0.999 does make a difference The newly introduced metrics *are* interesting, and the theoretical justifications *are* sensible. The paper would be even better if the applied motivations were better described. Since the proposed method does have the potential to be applied in industrial/applied scenarios, it is slightly disappointing that it is presented as another academic exercise. minor remark: it is not clear why the factor (1/K) is needed in the definition of SCE since the weights are already summing up to one -- this makes SCE comparisons between datasets with different number fo classes more difficult.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Measuring Calibration in Deep Learning
Overconfidence and underconfidence in machine learning classifiers is measured by calibration: the degree to which the probabilities predicted for each class match the accuracy of the classifier on that prediction. We propose two new measures for calibration, the Static Calibration Error (SCE) and Adaptive Calibration Error (ACE). These measures take into account every prediction made by a model, in contrast to the popular Expected Calibration Error.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



After an interesting review of calibration methods, the paper describes two new methods for assessing calibration. The first method, SCE, is an extension of the usual ECE to the multi class setting. The second method, ACE, is a slight variation where bins are computed adaptively. The paper is interesting and relatively well written. Although the contribution is rather simple (can be describe in less than a page), I can see myself using the SCE/ACE metrics in the near future. In order to strengthen the submission, I still feel that the authors should try to describe why the newly introduced metrics can help in applied scenarios. (1) OK, the ECE omits a lot of important predictions (e.g. fig 1) --> give a real application where this matters (2) OK, adaptive binning seems a sensible approach. --> give a real application where a difference between 0.99 and 0.999 does make a difference The newly introduced metrics *are* interesting, and the theoretical justifications *are* sensible. The paper would be even better if the applied motivations were better described. Since the proposed method does have the potential to be applied in industrial/applied scenarios, it is slightly disappointing that it is presented as another academic exercise. minor remark: it is not clear why the factor (1/K) is needed in the definition of SCE since the weights are already summing up to one -- this makes SCE comparisons between datasets with different number fo classes more difficult.
assistant
After an interesting review of calibration methods, the paper describes two new methods for assessing calibration. The first method, SCE, is an extension of the usual ECE to the multi class setting. The second method, ACE, is a slight variation where bins are computed adaptively. The paper is interesting and relatively well written. Although the contribution is rather simple (can be describe in less than a page), I can see myself using the SCE/ACE metrics in the near future. In order to strengthen the submission, I still feel that the authors should try to describe why the newly introduced metrics can help in applied scenarios. (1) OK, the ECE omits a lot of important predictions (e.g. fig 1) --> give a real application where this matters (2) OK, adaptive binning seems a sensible approach. --> give a real application where a difference between 0.99 and 0.999 does make a difference The newly introduced metrics *are* interesting, and the theoretical justifications *are* sensible. The paper would be even better if the applied motivations were better described. Since the proposed method does have the potential to be applied in industrial/applied scenarios, it is slightly disappointing that it is presented as another academic exercise. minor remark: it is not clear why the factor (1/K) is needed in the definition of SCE since the weights are already summing up to one -- this makes SCE comparisons between datasets with different number fo classes more difficult.