A combination of Model Ensembling methods that is extremely useful for increasing accuracy of Kaggle's submission. For more information: http://mlwave.com/kaggle-ensembling-guide/
$ python correlations.py ./samples/method1.csv ./samples/method2.csv
Finding correlation between: ./samples/method1.csv and ./samples/method2.csv
Column to be measured: Label
Pearson's correlation score: 0.67898
Kendall's correlation score: 0.66667
Spearman's correlation score: 0.71053
$ python kaggle_vote.py "./samples/method*.csv" "./samples/kaggle_vote.csv"
parsing: ./samples/method1.csv
parsing: ./samples/method2.csv
parsing: ./samples/method3.csv
wrote to ./samples/kaggle_vote.csv
$ python kaggle_rankavg.py "./samples/method*.csv" "./samples/kaggle_rankavg.csv"
parsing: ./samples/method1.csv
parsing: ./samples/method2.csv
parsing: ./samples/method3.csv
wrote to ./samples/kaggle_rankavg.csv
$ python kaggle_avg.py "./samples/method*.csv" "./samples/kaggle_avg.csv"
parsing: ./samples/method1.csv
parsing: ./samples/method2.csv
parsing: ./samples/method3.csv
wrote to ./samples/kaggle_avg.csv
$ python kaggle_geomean.py "./samples/method*.csv" "./samples/kaggle_geomean.csv"
parsing: ./samples/method1.csv
parsing: ./samples/method2.csv
parsing: ./samples/method3.csv
wrote to ./samples/kaggle_geomean.csv
==> ./samples/method1.csv <==
ImageId,Label
1,1
2,0
3,9
4,9
5,3
==> ./samples/method2.csv <==
ImageId,Label
1,2
2,0
3,6
4,2
5,3
==> ./samples/method3.csv <==
ImageId,Label
1,2
2,0
3,9
4,2
5,3
==> ./samples/kaggle_avg.csv <==
ImageId,Label
1,1.666667
2,0.000000
3,8.000000
4,4.333333
5,3.000000
==> ./samples/kaggle_rankavg.csv <==
ImageId,Label
1,0.25
2,0.0
3,1.0
4,0.5
5,0.75
==> ./samples/kaggle_vote.csv <==
ImageId,Label
1,2
2,0
3,9
4,2
5,3
==> ./samples/kaggle_geomean.csv <==
ImageId,Label
1,1.587401
2,0.000000
3,7.862224
4,3.301927
5,3.000000