activity
20192024
most citedClassifier Calibration: A survey on how to assess and improve predicted class probabilities

194 citations · 357 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC2024

Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care

Jeffrey N. Clark, Matthew Wragg, Emily Nielsen +7

There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and…

cs.LG2022★ 1 cited

The Weak Supervision Landscape

Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesus Cid-Sueiro +3

Many ways of annotating a dataset for machine learning classification tasks that go beyond the usual class labels exist in practice. These are of interest as they can simplify or f…

cs.LG2021★ 194 cited

Classifier Calibration: A survey on how to assess and improve predicted class probabilities

Telmo Silva Filho, Hao Song, Miquel Perello-Nieto +3

This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the…

cs.LG2019★ 162 cited

Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration

Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp +3

Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperatu…

cs.LG2019

HyperStream: a Workflow Engine for Streaming Data

Tom Diethe, Meelis Kull, Niall Twomey +5

This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabili…