Editing Machine Learning Meetup Notes:2011-4-13

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Anthony Goldbloom from Kaggle Visits
Anthony Goldbloom from Kaggle Visits


*Link to his talk: [https://www.noisebridge.net/images/e/ed/Goldbloom_-_Predictive_modeling_competitions_-_April_2011.ppt PPT presentation]
**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.
*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 [http://cran.r-project.org/web/packages/caret/ caret] package in R to deal with random forests.
**Used [http://cran.r-project.org/web/packages/caret/ caret] package in R to deal with random forests.
**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.
*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: lots of american users
**Matlab
**SAS
**Weka
**SPSS
**Python: although it's lower on the list, people are successful with it
*R packages used: Caret, RFE, GLM, NNET, Forecast
*Heritage Prize
**Real shit is going down may 4th, with release of all datasets.
**Ends in 2 years. No rush.
**Four prizes in total, given out throughout the next two years.
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