Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap

From MaRDI portal
Publication:961845

DOI10.1016/j.csda.2009.04.009zbMath1453.62126OpenAlexW2073792037MaRDI QIDQ961845

B. E. Eshmatov

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.04.009



Related Items

Group regularization for zero-inflated poisson regression models with an application to insurance ratemaking, Discriminant analysis with Gaussian graphical tree models, Mean field variational Bayesian inference for support vector machine classification, Improvement of ID3 algorithm based on simplified information entropy and coordination degree, Unnamed Item, Adversarial Machine Learning: Bayesian Perspectives, Approximate Bayesian inference in spatial GLMM with skew normal latent variables, An experimental comparison of cross-validation techniques for estimating the area under the ROC curve, On the performance of the flexible maximum entropy distributions within partially adaptive estimation, Estimation of predictive performance in high-dimensional data settings using learning curves, Functional methods for classification of different petrographic varieties by means of reflectance spectra, Block-regularized repeated learning-testing for estimating generalization error, Binary trees for dissimilarity data, Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods, Fast robust estimation of prediction error based on resampling, Mean-variance-skewness-entropy measures: a multi-objective approach for portfolio selection, Support vector regression for polyhedral and missing data, A Robust Alternative to the Schemper-Henderson Estimator of Prediction Error, On the generative-discriminative tradeoff approach: interpretation, asymptotic efficiency and classification performance, Markov cross-validation for time series model evaluations, Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter, Gradient methods for solving Stackelberg games, Adversarial classification: an adversarial risk analysis approach, Discriminant analysis for discrete variables derived from a tree-structured graphical model, Learning quantities of interest from dynamical systems for observation-consistent inversion


Uses Software


Cites Work