| A study of cross-validation and bootstrap for accuracy estimation and model selection R Kohavi Ijcai 14 (2), 1137-1145, 1995 | 19621 | 1995 |
| Wrappers for feature subset selection R Kohavi, GH John Artificial intelligence 97 (1-2), 273-324, 1997 | 12704 | 1997 |
| An empirical comparison of voting classification algorithms: Bagging, boosting, and variants E Bauer, R Kohavi Machine learning 36 (1), 105-139, 1999 | 4010 | 1999 |
| Irrelevant features and the subset selection problem GH John, R Kohavi, K Pfleger Machine learning proceedings 1994, 121-129, 1994 | 3852 | 1994 |
| Supervised and unsupervised discretization of continuous features J Dougherty, R Kohavi, M Sahami Machine learning proceedings 1995, 194-202, 1995 | 3296 | 1995 |
| Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. R Kohavi Kdd 96, 202-207, 1996 | 2660 | 1996 |
| Glossary of terms R Kohavi Machine learning 30, 271-274, 1998 | 2106 | 1998 |
| The case against accuracy estimation for comparing induction algorithms. FJ Provost, T Fawcett, R Kohavi ICML 98, 445-453, 1998 | 2090* | 1998 |
| The power of decision tables R Kohavi European conference on machine learning, 174-189, 1995 | 1186 | 1995 |
| Bias plus variance decomposition for zero-one loss functions R Kohavi, DH Wolpert ICML 96, 275-283, 1996 | 1128 | 1996 |
| Controlled experiments on the web: survey and practical guide R Kohavi, R Longbotham, D Sommerfield, RM Henne Data mining and knowledge discovery 18 (1), 140-181, 2009 | 1116 | 2009 |
| Real world performance of association rule algorithms Z Zheng, R Kohavi, L Mason Proceedings of the seventh ACM SIGKDD international conference on Knowledge …, 2001 | 752 | 2001 |
| Online controlled experiments and A/B tests R Kohavi, R Longbotham Encyclopedia of machine learning and data science, 1-13, 2023 | 640 | 2023 |
| Online controlled experiments at large scale R Kohavi, A Deng, B Frasca, T Walker, Y Xu, N Pohlmann Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 558 | 2013 |
| Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology. R Kohavi, D Sommerfield KDD 95, 192-197, 1995 | 547 | 1995 |
| Practical guide to controlled experiments on the web: listen to your customers not to the hippo R Kohavi, RM Henne, D Sommerfield Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007 | 529 | 2007 |
| Emerging trends in business analytics R Kohavi, NJ Rothleder, E Simoudis Communications of the ACM 45 (8), 45-48, 2002 | 512 | 2002 |
| Trustworthy online controlled experiments: A practical guide to a/b testing R Kohavi, D Tang, Y Xu Cambridge University Press, 2020 | 510 | 2020 |
| Wrappers for performance enhancement and oblivious decision graphs R Kohavi stanford university, 1996 | 509 | 1996 |
| Data mining using MLC++: A machine learning library in C++ R Kohavi, D Sommerfield, J Dougherty Int. J. on Artificial Intelligence Tools 6 (4), 537-566, 1997 | 500 | 1997 |
George JohnStanford UniversityVerified email at cs.stanford.edu
