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"

8 / 8 papers shown

Title
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 39 0 0 20 Feb 2024
Is rotation forest the best classifier for problems with continuous features? A. Bagnall Michael Flynn J. Large Jason Lines A. Bostrom G. Cawley 41 39 0 18 Sep 2018
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis A. Benavoli Giorgio Corani J. Demšar Marco Zaffalon BDL 62 424 0 14 Jun 2016
Comparison of 14 different families of classification algorithms on 115 binary datasets Jacques Wainer 38 85 0 02 Jun 2016
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science Randal S. Olson Nathan Bartley Ryan J. Urbanowicz J. Moore 48 530 0 20 Mar 2016
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin 809 38,961 0 09 Mar 2016
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 57 315 0 28 Jan 2016
Feature selection in omics prediction problems using cat scores and false nondiscovery rate control M. Ahdesmaki K. Strimmer 108 144 0 11 Mar 2009