Contributors: Krieger Benjamin, Zell Kilian, Jegou Sam
Competition: Higgs Boson Machine Learning Challenge
Our best model was a ridge regression, with a polynomial augmentation of degree 5 and some other feature augmentations.
All the outliers were set to the mean of their corresponding features, before the standardization process (0 mean, 1 standard deviation).
It has been tried to remove the features that had too much outliers (~90%), or separate the dataset in function of the indicator variable JET, but it decreased our accuracy.
Our model is a simple ridge regression, with regularization of
- a polynomial augmentation of degree 5
- some other augmentations for the first features, with cosines, sinus, sinc, and others to add non linearity
Then we project the prediction on {-1,1} with a threshold at
Put the files run.ipynb
and implementations.py
in the main folder.
In the same folder, create a folder 'Data' containing the data file 'sample-submission.csv'.
Run the file run.py
. It will generate in your current folder a file 'submission.csv', containing the prediction of our model.
Machine learning course at EPFL:
Description of the dataset: https://higgsml.lal.in2p3.fr/files/2014/04/documentation_v1.8.pdf