6f16f0ef58
* Fix various typos * Add override to functions that are overridden gcc gives warnings about functions that are being overridden by not being marked as oveirridden. This fixes it. * Use bst_float consistently Use bst_float for all the variables that involve weight, leaf value, gradient, hessian, gain, loss_chg, predictions, base_margin, feature values. In some cases, when due to additions and so on the value can take a larger value, double is used. This ensures that type conversions are minimal and reduces loss of precision.
Highlights
Higgs challenge ends recently, xgboost is being used by many users. This list highlights the xgboost solutions of players
- Blogpost by phunther: Winning solution of Kaggle Higgs competition: what a single model can do
- The solution by Tianqi Chen and Tong He Link
Guide for Kaggle Higgs Challenge
This is the folder giving example of how to use XGBoost Python Module to run Kaggle Higgs competition
This script will achieve about 3.600 AMS score in public leaderboard. To get start, you need do following step:
- Compile the XGBoost python lib
cd ../..
make
-
Put training.csv test.csv on folder './data' (you can create a symbolic link)
-
Run ./run.sh
Speed
speedtest.py compares xgboost's speed on this dataset with sklearn.GBM
Using R module
- Alternatively, you can run using R, higgs-train.R and higgs-pred.R.