Please Note: This seminar will be given in-person.
Room: DC 1302
Some Comments on CV
Cross-validation is ubiquitous in data science, and is used for both model selection and assessment. Yet in some regards it is poorly understood. In this talk we discuss three aspects of CV.
- What CV estimates?
- Confidence intervals for prediction error using nested CV.
- OOB error for random-forests and standard error estimates.
This talk is dedicated to Leo Breiman and Colin Mallows; it is based on joint work with Stephen Bates, Samyak Rajanala and Rob Tibshirani.
Trevor Hastie is the John A. Overdeck Professor of Mathematical Sciences, Professor of Statistics and Professor of Biomedical Data Science at Stanford University. Dr. Hastie is known for his research in applied statistics, particularly in the fields of statistical modeling, bioinformatics and machine learning. He has published six books and over 200 research articles in these areas. Prior to joining Stanford University in 1994, Dr. Hastie worked at AT&T Bell Laboratories for 9 years, where he contributed to the development of the statistical modeling environment popular in the R computing system. He received a B.Sc. (hons) in statistics from Rhodes University in 1976, a M.Sc. from the University of Cape Town in 1979, and a Ph.D. from Stanford in 1984. In 2018 he was elected to the United States National Academy of Sciences.