Machine Learning Meetup Notes:2011-4-13: Difference between revisions
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*Kaggle splits test dataset into two, uses half for leaderboard. | *Kaggle splits test dataset into two, uses half for leaderboard. | ||
*Often score difference between winning model and second place is not statistically significant. So they award prizes to top few. Might impose restrictions on execution time of model. | *Often score difference between winning model and second place is not statistically significant. So they award prizes to top few. Might impose restrictions on execution time of model. | ||
*Performance bottoms out in competitions within a few weeks in general. This seems to be due to all the information being "squeezed" out of the dataset at that point. | |||
Revision as of 20:11, 13 April 2011
Anthony Goldbloom from Kaggle Visits
- Guy used random forests to win HIV competition. Word "random forests" is trademarked. Dude taught himself machine learning from watching youtube videos. Random forests are pretty robust to new data.
- Used caret package in R to deal with random forests.
- Kaggle splits test dataset into two, uses half for leaderboard.
- Often score difference between winning model and second place is not statistically significant. So they award prizes to top few. Might impose restrictions on execution time of model.
- Performance bottoms out in competitions within a few weeks in general. This seems to be due to all the information being "squeezed" out of the dataset at that point.