This code was written by Paul Duan ([email protected]) and Benjamin Solecki ([email protected]) and tuned to this challenge by Fran Lozano ([email protected]). See reference repository It provides my solution to the ETS Asset Management Factory Challenge.
[python] classifier.py [-h] [-d] [-i ITER] [-f OUTPUTFILE] [-g] [-m] [-n] [-s] [-v] [-w]
Parameters for the script.
optional arguments:
-h, --help show this help message and exit
-d, --diagnostics Compute diagnostics.
-i ITER, --iter ITER Number of iterations for averaging.
-f OUTPUTFILE, --outputfile OUTPUTFILE
Name of the file where predictions are saved.
-g, --grid-search Use grid search to find best parameters.
-m, --model-selection
Use model selection.
-n, --no-cache Use cache.
-s, --stack Use stacking.
-v, --verbose Show computation steps.
-w, --fwls Use metafeatures.
To directly generate predictions on the test set without computing CV metrics, simply run:
python classifier.py -i0 -f[output_filename]
This content is released under the MIT Licence.