Add to ORKGA multiclass classifier is said to be top-label calibrated if the reported probability for the predicted class -- the top-label -- is calibrated, conditioned on the top-label. This conditioning on the top-label is absent in the closely related and popular notion of confidence calibration, which we argue makes confidence calibration difficult to interpret for decision-making. We propose top-label calibration as a rectification of confidence calibration. Further, we outline a multiclass-to-binary (M2B) reduction framework that unifies confidence, top-label, and class-wise calibration, among others. As its name suggests, M2B works by reducing multiclass calibration to numerous binary calibration problems, each of which can be solved using simp...
Learning probabilistic classification and prediction models that generate accurate probabilities is ...
Deep neural networks (DNNs) have made great strides in pushing the state-of-the-art in several chall...
Despite their incredible performance, it is well reported that deep neural networks tend to be overo...
For many applications of probabilistic classifiers it is important that the predicted confidence vec...
Accurate calibration of probabilistic predictive models learned is critical for many practical predi...
A much studied issue is the extent to which the confidence scores provided by machine learning algor...
Machine learning classifiers typically provide scores for the different classes. These scores are su...
Despite impressive accuracy, deep neural networks are often miscalibrated and tend to overly confide...
In safety-critical applications a probabilistic model is usually required to be calibrated, i.e., to...
For safety-related applications, it is crucial to produce trustworthy deep neural networks whose pre...
Calibrating a classification system consists in transforming the output scores, which somehow state t...
Recent studies have revealed that, beyond conventional accuracy, calibration should also be consider...
Introduced as a notion of algorithmic fairness, multicalibration has proved to be a powerful and ver...
The deployment of machine learning classifiers in high-stakes domains requires well-calibrated confi...
We study confidence-rated prediction in a binary classification setting, where the goal is to design...
Learning probabilistic classification and prediction models that generate accurate probabilities is ...
Deep neural networks (DNNs) have made great strides in pushing the state-of-the-art in several chall...
Despite their incredible performance, it is well reported that deep neural networks tend to be overo...
For many applications of probabilistic classifiers it is important that the predicted confidence vec...
Accurate calibration of probabilistic predictive models learned is critical for many practical predi...
A much studied issue is the extent to which the confidence scores provided by machine learning algor...
Machine learning classifiers typically provide scores for the different classes. These scores are su...
Despite impressive accuracy, deep neural networks are often miscalibrated and tend to overly confide...
In safety-critical applications a probabilistic model is usually required to be calibrated, i.e., to...
For safety-related applications, it is crucial to produce trustworthy deep neural networks whose pre...
Calibrating a classification system consists in transforming the output scores, which somehow state t...
Recent studies have revealed that, beyond conventional accuracy, calibration should also be consider...
Introduced as a notion of algorithmic fairness, multicalibration has proved to be a powerful and ver...
The deployment of machine learning classifiers in high-stakes domains requires well-calibrated confi...
We study confidence-rated prediction in a binary classification setting, where the goal is to design...
Learning probabilistic classification and prediction models that generate accurate probabilities is ...
Deep neural networks (DNNs) have made great strides in pushing the state-of-the-art in several chall...
Despite their incredible performance, it is well reported that deep neural networks tend to be overo...