Results 11 to 20 of about 8,137,182 (304)
Cross-Validation Visualized: A Narrative Guide to Advanced Methods
Machine Learning and Knowledge ExtractionThis study delves into the multifaceted nature of cross-validation (CV) techniques in machine learning model evaluation and selection, underscoring the challenge of choosing the most appropriate method due to the plethora of available variants.
Johannes Allgaier, Rüdiger Pryss
doaj +2 more sources
Consensus Features Nested Cross-Validation [PDF]
Bioinformatics, 2020 AbstractMotivationFeature selection can improve the accuracy of machine learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential
Parvandeh, Saeid +3 more
openaire +3 more sources
Cross-Validation: What Does It Estimate and How Well Does It Do It? [PDF]
Journal of the American Statistical Association, 2021 Cross-validation is a widely used technique to estimate prediction error, but its behavior is complex and not fully understood. Ideally, one would like to think that cross-validation estimates the prediction error for the model at hand, fit to the ...
Stephen Bates, T. Hastie, R. Tibshirani
semanticscholar +1 more source
Asymptotics of cross-validation
Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2020 62 pages, 3 tables; typos and minor ...
Austern, Morgane, Zhou, Wenda
openaire +3 more sources
A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging.
Radiology: Artificial Intelligence, 2023 Artificial intelligence (AI) is being increasingly used to automate and improve technologies within the field of medical imaging. A critical step in the development of an AI algorithm is estimating its prediction error through cross-validation (CV).
T. Bradshaw +3 more
semanticscholar +1 more source
Italian National Conference on Sensors, 2023 Nowadays, the solution to many practical problems relies on machine learning tools. However, compiling the appropriate training data set for real-world classification problems is challenging because collecting the right amount of data for each class is ...
Szilvia Szeghalmy, A. Fazekas
semanticscholar +1 more source
JMIR AI, 2023 Cross-validation remains a popular means of developing and validating artificial intelligence for health care. Numerous subtypes of cross-validation exist. Although tutorials on this validation strategy have been published and some with applied examples,
Drew Wilimitis, Colin G Walsh
semanticscholar +1 more source
Diagnostic and Prognostic Research, 2023 Background The performance of models for binary outcomes can be described by measures such as the concordance statistic (c-statistic, area under the curve), the discrimination slope, or the Brier score. At internal validation, data resampling techniques,
A. Geroldinger +3 more
semanticscholar +1 more source
SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer
Frontiers in Nanotechnology, 2022 Cancer is the unregulated development of abnormal cells in the human body system. Cervical cancer, also known as cervix cancer, develops on the cervix’s surface. This causes an overabundance of cells to build up, eventually forming a lump or tumour. As a
Sashikanta Prusty, S. Patnaik, S. Dash
semanticscholar +1 more source
Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection [PDF]
Entropy, 2021 Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state-of-the-art results in many prediction tasks. Despite its popularity, the GBM framework suffers from a fundamental flaw in its base learners. Specifically,
Afek Ilay Adler, Amichai Painsky
semanticscholar +1 more source