Assessing Model Fit by Cross-Validation

By Douglas M. Hawkins, S. Basak, D. Mills. 2003

724 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Applied Predictive Modeling (2013, background): “Several researchers (Molinaro 2005; Martin and Hirschberg 1996; Hawkins et al. 2003) show that validation using a single test set can be a poor choice.”
Predictive Association between Biotic Stress Traits and Eco-Geographic Data for Wheat and Barley Landraces (2011, methodology): “The calibration of the model parameters was made with cross-validation (Hawkins et al., 2003).”
Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods (2015, background): “However, when the data-set are limited, some types of cross-validation techniques are usually carried out (Hawkins et al. 2003).”
Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation (2014, neutral): “The hold-out method separates test and training data unmistakably since the test data are undoubtedly removed from the training data [38].”
On further application of r m2 as a metric for validation of QSAR models (2009, methodology): “[20,32,57] already showed that for small sample size, LOO cross-validation with application of model selection process at each cycle of iterations during validation performs better than the traditional splitting process for validation.”