Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation

Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets

(2017) Vincenzo Lagani et al.   Journal of Statistical Software

Chemically intuited, large-scale screening of MOFs by machine learning techniques

(2017) Giorgos Borboudakis et al.   npj Computational Materials

MatureP: prediction of secreted proteins with exclusive information from their mature regions

(2017) Georgia Orfanoudaki et al.   Scientific Reports

Performance-Estimation Properties of Cross-Validation-Based Protocols with Simultaneous Hyper-Parameter Optimization

(2015) Ioannis Tsamardinos et al.   International Journal on Artificial Intelligence Tools

Regularization Paths for Generalized Linear Models via Coordinate Descent

(2015) Jerome Friedman et al.   Journal of Statistical Software

Bias correction for selecting the minimal-error classifier from many machine learning models

(2014) Ying Ding et al.   BIOINFORMATICS

Cross-validation pitfalls when selecting and assessing regression and classification models

(2014) Damjan Krstajic et al.   Journal of Cheminformatics

Correcting the Optimal Resampling-Based Error Rate by Estimating the Error Rate of Wrapper Algorithms

(2013) Christoph Bernau et al.   BIOMETRICS

Multiple-rule bias in the comparison of classification rules

(2011) Mohammadmahdi R. Yousefi et al.   BIOINFORMATICS

A bias correction for the minimum error rate in cross-validation

(2009) Ryan J. Tibshirani et al.   Annals of Applied Statistics

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

(2009) Anne-Laure Boulesteix et al.   BMC Medical Research Methodology