Nested cross-validation when selecting classifiers is overzealous for most practical applications

25 September 2018

Papers citing "Nested cross-validation when selecting classifiers is overzealous for most practical applications"

7 / 7 papers shown

Title
Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments Mahsa Amiri Zahra Zanjani Foumani Penghui Cao Lorenzo Valdevit Ramin Bostanabad AI4CE 24 1 0 30 Aug 2024
Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study Elodie Germani Nikhil Baghwat Mathieu Dugré Rémi Gau A. Montillo Kevin Nguyen Andrzej Sokolowski Madeleine Sharp J B Poline Tristan Glatard 26 0 0 20 Feb 2024
Video-based Automatic Lameness Detection of Dairy Cows using Pose Estimation and Multiple Locomotion Traits H. Russello R. V. D. Tol M. Holzhauer Eldert J. van Henten Gert Kootstra 15 13 0 10 Jan 2024
A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets Jacques Wainer 13 10 0 09 Aug 2022
Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow Jeeyung Kim Mengtian Jin Youkow Homma A. Sim W. Kroeger K. Wu AI4TS 16 0 0 19 May 2022
Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data Lingjing Jiang N. Haiminen A. Carrieri Shi Huang Yoshiki Vazquez-Baeza L. Parida Ho-Cheol Kim Austin D. Swafford R. Knight L. Natarajan 20 7 0 30 Nov 2020
Automating biomedical data science through tree-based pipeline optimization Randal S. Olson Ryan J. Urbanowicz Peter C. Andrews Nicole A. Lavender L. C. Kidd J. Moore AI4CE 33 311 0 28 Jan 2016