Please Note: This seminar will be given in-person.
|
Distinguished Lecture
Trevor
Hastie 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
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.