Bibliography

[1]
J. Pizarro, E. Guerrero and P. L. Galindo. Multiple Comparison Procedures Applied to Model Selection. Neurocomputing 48, 155–173 (2002). ↩1
[2]
D. Crucitti and A. Mosquera Orgueira. Train–Test Splits Matter More for Evaluation than for Performance. Journal of Cheminformatics (2026). ↩1 ↩2 ↩3
[3]
S. Arlot and A. Celisse. A Survey of Cross-Validation Procedures for Model Selection. Statistics Surveys 4 (2010). ↩1 ↩2
[4]
G. C. Cawley and N. L. Talbot. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010). ↩1
[5]
J. Camacho. A Set of Rules for Model Validation. Journal of Chemometrics 40, e70110 (2026). ↩1
[6]
A. Vabalas, E. Gowen, E. Poliakoff and A. J. Casson. Machine Learning Algorithm Validation with a Limited Sample Size. PLOS ONE 14, e0224365 (2019). ↩1
[7]
[8]
S. Raschka. Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning (2018). ↩1
[9]
M. Daszykowski, B. Walczak and D. L. Massart. On the Optimal Partitioning of Data with K-Means, Growing K-Means, Neural Gas, and Growing Neural Gas. Journal of Chemical Information and Computer Sciences 42, 1378–1389 (2002). ↩1
[10]
X. Bian, W. Yang, K. Zhang, Q. Zhang, W. Tian and G. van Kollenburg. A Review on Sample Subset Selection Methods for Multivariate Modelling. Chemometrics and Intelligent Laboratory Systems 265, 105493 (2025). ↩1
[11]
Y. Xu and R. Goodacre. On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning. Journal of Analysis and Testing 2, 249–262 (2018). ↩1
[12]
T. Puzyn, A. Mostrag-Szlichtyng, A. Gajewicz, M. Skrzyński and A. P. Worth. Investigating the Influence of Data Splitting on the Predictive Ability of QSAR/QSPR Models. Structural Chemistry 22, 795–804 (2011). ↩1
[13]
S. Bates, T. Hastie and R. Tibshirani. Cross-Validation: What Does It Estimate and How Well Does It Do It? Journal of the American Statistical Association 119, 1434–1445 (2024). ↩1
[14]
D. R. Roberts, V. Bahn, S. Ciuti, M. S. Boyce, J. Elith, G. Guillera-Arroita, S. Hauenstein, J. J. Lahoz-Monfort, B. Schröder, W. Thuiller, D. I. Warton, B. A. Wintle, F. Hartig and C. F. Dormann. Cross-validation Strategies for Data with Temporal, Spatial, Hierarchical, or Phylogenetic Structure. Ecography 40, 913–929 (2017). ↩1 ↩2 ↩3 ↩4 ↩5
[15]
C. Bergmeir and J. M. Benítez. On the Use of Cross-Validation for Time Series Predictor Evaluation. Information Sciences 191, 192–213 (2012). ↩1
[16]
M. M. López de Prado. Advances in Financial Machine Learning (Wiley, Hoboken, New Jersey, 2018). ↩1
[17]
R. May, H. Maier and G. Dandy. Data Splitting for Artificial Neural Networks Using SOM-based Stratified Sampling. Neural Networks 23, 283–294 (2010). ↩1
[18]
R. W. Kennard and L. A. Stone. Computer Aided Design of Experiments. Technometrics 11, 137–148 (1969). ↩1
[19]
R. Galvao, M. Araujo, G. Jose, M. Pontes, E. Silva and T. Saldanha. A Method for Calibration and Validation Subset Partitioning. Talanta 67, 736–740 (2005). ↩1
[20]
A. Saptoro, M. O. Tadé and H. Vuthaluru. A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models. Chemical Product and Process Modeling 7 (2012). ↩1
[21]
C. L. Morais, M. C. Santos, K. M. Lima and F. L. Martin. Improving Data Splitting for Classification Applications in Spectrochemical Analyses Employing a Random-Mutation Kennard-Stone Algorithm Approach. Bioinformatics 35, 5257–5263 (2019). ↩1