On the use of cross-validation to assess performance in multivariate prediction

By Philip Jonathan, Wojtek Janusz Krzanowski, W. V. McCarthy. 2000

48 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Applied Mathematics, Statistics and Probability

How researchers see this paper

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

In Analytical Chemistry

1. Andreas Zell
2. Jaewook Lee
3. Huisu Jang

In Applied Mathematics

1. Jaewook Lee
2. Huisu Jang

In Statistics and Probability

1. Jaewook Lee
2. Huisu Jang

Citations in the corpus

• Interpreting linear support vector machine models with heat map molecule coloring (2011, methodology): “To evaluate the performance on each data set, we used a 5-fold two-deep cross-validation [22] which was repeated two times.”
• An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information (2018, methodology): “Total prediction accuracy is combined after the procedure is repeated for each part to leave [30], [31].”
• Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation (2014, unknown): “two-deep cross-validation [5,8], or cross-model valid-”