# Solutions sharing

Congratulations to the winners! This competition was pretty interesting. Here is our approach:
Before merging with Abhishek, I used two models: R svm with spectral features and R gbm with derivative and spatial features. I used grid search to select features for svm by each target separately. Gbm did not give good result, but it gave good improvement in the ensemble with svm. One more thing I noticed: multiplying by constant improves the score. The only problem this constant was 1.02 on CV and 1.08 on LB. At the end I chose one submission with constant 1.02 and one with 1.08.
After merging, I got neural network model from Abhishek and it gave another improvement on CV and LB. It turns out that on CV neural network was the best model (though on LB svm was better).
In principle, my purpose was to make model simple and robust (2nd on Public, 7th on Private, very good for such kind of competition), that's why I used Landscapes (thanks to BreakfastPirate for it!) for CV score calculations.