Results 1 to 10 of about 160,288 (166)

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. [PDF]

BMC Genomics, 2020
AbstractBackgroundTo evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet.
Chicco D, Jurman G.
europepmc   +6 more sources

X-ray is not inferior to CT in terms of F1 score in the diagnosis of foreign body aspiration: a recall, precision and F1 score performance analysis based on bronchoscopically proven cases. [PDF]

J Pediatr (Rio J)
In this study, we aimed to evaluate the diagnostic accuracy of X-ray and CT by using the F1 score with its non-inferiority margin in patients who underwent bronchoscopy with suspected diagnoses of foreign body aspiration (FBA).All children aged under 18 who underwent bronchoscopy with suspected diagnoses of FBA between June 2020 and December 2023 were ...
Sarac F, Yazici M.
europepmc   +4 more sources

Calibrating F1 Scores for Fair Performance Comparison of Binary Classification Models With Application to Student Dropout Prediction

IEEE Access
The F1 score has been widely used to measure the performance of machine learning models. However, it is variant to the ratio of the positive class in the training data, $\pi $ .
Hyeon Gyu Kim, Yoohyun Park
doaj   +2 more sources

Predicting breast cancer using machine learning classifiers and enhancing the output by combining the predictions to generate optimal F1-score

Biomedical and Biotechnology Research Journal, 2021
Background: Biomedical field has gained a lot of interest from active researchers today. Treating various diseases prevailing among the world has believed to bring huge insight in the today's research world.
Disha Harshadbhai Parekh, Vishal Dahiya
doaj   +1 more source

STUDY TIME CLASSIFICATION OF MATHEMATICS AND INFORMATION TECHNOLOGY DEPARTMENT OF KALIMANTAN INSTITUTE OF TECHNOLOGY USING NAÏVE BAYES ALGORITHM

Barekeng, 2023
Institut Teknologi Kalimantan (ITK) is one of the state universities in Indonesia which has 5 majors, one of them is the Department of Mathematics and Information Technology (JMTI).
Fatrysia Wikarya Sucipto, Ramadhan Paninggalih, Indira Anggriani   +2 more
doaj   +1 more source

90% F1 Score in Relation Triple Extraction: Is it Real?

Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP, 2023
Accepted in GenBench workshop @ EMNLP ...
Saini, Pratik, Pal, Samiran, Nayak, Tapas, Bhattacharya, Indrajit   +3 more
openaire   +2 more sources

Estimating the Uncertainty of Average F1 Scores [PDF]

Proceedings of the 2015 International Conference on The Theory of Information Retrieval, 2015
In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data.
Dell Zhang, Jun Wang 0012, Xiaoxue Zhao
openaire   +1 more source

A Bayesian Hierarchical Model for Comparing Average F1 Scores [PDF]

2015 IEEE International Conference on Data Mining, 2015
In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data.
Dell Zhang, Jun Wang 0012, Xiaoxue Zhao, Xiaoling Wang 0004   +3 more
openaire   +1 more source

sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification

Trans. Mach. Learn. Res., 2021
Multiclass multilabel classification is the task of attributing multiple labels to examples via predictions. Current models formulate a reduction of the multilabel setting into either multiple binary classifications or multiclass classification, allowing for the use of existing loss functions (sigmoid, cross-entropy, logistic, etc.).
Bénédict, G., Koops, H.V., Odijk, D., de Rijke, M.   +3 more
openaire   +4 more sources

Comparative analysis of models predicting the risks of early poor outcome of deceased-donor liver transplantation: a retrospective single-center study

Трансплантология (Москва), 2023
Rationale. The risk of early graft loss determines the specifics and plan of anesthesiological assistance, intensive therapy, and overall the feasibility of liver transplantation.
A. I. Sushkov, M. V. Popov, V. S. Rudakov, D. S. Svetlakova, A. N. Pashkov, A. S. Lukianchikova, M. Muktarzhan, K. K. Gubarev, V. E. Syutkin, A. I. Artemyev, S. E. Voskanyan   +10 more
doaj   +1 more source