This project is dedicated to stimulate research and reveal the state-of-the art in "model
selection" by organizing a competition followed by a workshop. Model selection is a
problem in statistics, machine learning, and data mining. Given training data consisting
of input-output pairs, a model is built to predict the output from the input, usually by
fitting adjustable parameters. Many predictive models have been proposed to perform
such tasks, including linear models, neural networks, trees, and kernel methods. Finding
methods to optimally select models, which will perform best on new test data, is the
object of this project. The competition will help identifying accurate methods of model
assessment, which may include variants of the well-known cross-validation methods and
novel techniques based on learning theoretic performance bounds. Such methods are of
great practical importance in pilot studies, for which it is essential to know precisely how
well desired specifications are met.
Challenge Winners
| Best average rank |
Roman Lutz
with
|
| Best average score |
Gavin Cawley
with
|
Individual Dataset Winners
Statistics
| Entrants | 147 |
|---|
| Entries | 4233 |
|---|
| Valid challenge entries (*) | 251 |
|---|
(*) Valid entries include all results for all datasets
