Results 11 to 20 of about 1,347,746 (312)
Confidence Intervals for the F1 Score: A Comparison of Four Methods
, 2023 31 pages, 3 ...
Lam, Kevin Fu Yuan +2 more
openaire +3 more sources
Un intruso de otro mundo: F1-score.
Revista Electrónica AnestesiaREl F1-score, también llamado F-score o medida F, es un estimador de la capacidad de clasificación de una prueba que se usa con frecuencia en la ciencia de datos y en los algoritmos de inteligencia artificial y que puede ser de utilidad para la valoración de las pruebas diagnósticas.
Molina, Manuel
core +4 more sources
Diagnostics, 2022 Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease.
Mardhiyati Mohd Yunus +8 more
doaj +1 more source
Automated Drone Detection Using YOLOv4
Drones, 2021 Drones are increasing in popularity and are reaching the public faster than ever before. Consequently, the chances of a drone being misused are multiplying.
Subroto Singha, Burchan Aydin
doaj +1 more source
Anomaly Detection: How to Artificially Increase Your F1-Score with a Biased Evaluation Protocol [PDF]
, 2021 Anomaly detection is a widely explored domain in machine learning. Many models are proposed in the literature, and compared through different metrics measured on various datasets. The most popular metrics used to compare performances are F1-score, AUC and AVPR. In this paper, we show that F1-score and AVPR are highly sensitive to the contamination rate.
Damien Fourure +3 more
openaire +2 more sources
F1-score (F1-score) of different models.
, 2022 F1-score (F1-score) of different models.
Uyen Le (2228929) +2 more
core +1 more source
F1-score results for activity recognition models.
, 2022 Comparison of activity recognition model F1-score percentages for 50% and 90% sliding window overlaps across the four sliding window lengths (1, 5, 7.5, 10 seconds) for each work element (clear, delay, masticate, move, travel).
Robert F. Keefe (4760298) +1 more
core +1 more source
F1-score comparison of five boosting algorithms on test set.
, 2023 F1-score comparison of five boosting algorithms on test set.
Saurav Mallik (441729) +3 more
core +1 more source
F1-score for the cabinets-based taxonomy models.
, 2022 F1-score for the cabinets-based taxonomy models.
Magna Inácio (6141029) +3 more
core +1 more source
Benchmarking Low-Frequency Variant Calling With Long-Read Data on Mitochondrial DNA
Frontiers in Genetics, 2022 Background: Sequencing quality has improved over the last decade for long-reads, allowing for more accurate detection of somatic low-frequency variants. In this study, we used mixtures of mitochondrial samples with different haplogroups (i.e., a specific
Theresa Lüth +6 more
doaj +1 more source