The challenge is now over. But it remains open for post-challenge submissions!
IMPORTANT: Entries made since February 1st 2007 might be using validation data, now available for training.
"When everything fails, ask for additional domain knowledge"
is the current motto of machine learning. Therefore, assessing the real
added value of prior/domain knowledge is a both deep and practical question.
Most commercial data mining programs accept data pre-formatted as a table,
each example being encoded as a fixed set of features.
Is it worth spending time engineering elaborate features incorporating domain knowledge
and/or designing ad hoc algorithms?
Or else, can off-the-shelf programs working on simple features encoding the raw
data without much domain knowledge put out-of-business skilled data analysts?
In this challenge, the participants are allowed to compete in two tracks:
- The “prior knowledge” track, for which they will have access to the original raw data representation and as much knowledge as possible about the data.
- The “agnostic learning” track for which they will be forced to use a data representation encoding the raw data with dummy features.
Final results of August 1st, 2007
"Prior Knowledge" winner
Individual dataset winners
|Valid challenge entries (*)||316|
(*) Valid entries include all results for all datasets