Machine Learning Meetup Notes:2011-4-13: Difference between revisions
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*Chess rating competition: build a new rating system that more accurately produces the results. The performance still plateaued, but took longer. | *Chess rating competition: build a new rating system that more accurately produces the results. The performance still plateaued, but took longer. | ||
*Most users of kaggle are from computer science and statistics, followed by economics, math, biostats. | *Most users of kaggle are from computer science and statistics, followed by economics, math, biostats. | ||
*Tools people use: | |||
**R | |||
**Matlab | |||
**SAS | |||
**Weka | |||
**SPSS | |||
**Python |
Revision as of 20:17, 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.
- Chess rating competition: build a new rating system that more accurately produces the results. The performance still plateaued, but took longer.
- Most users of kaggle are from computer science and statistics, followed by economics, math, biostats.
- Tools people use:
- R
- Matlab
- SAS
- Weka
- SPSS
- Python