szjunma / kaggle-otto-group

Kaggle's Otto Group Product Classification Challenge

Kaggle's Otto Group Product Classification Challenge

Introduction

This is my code for kaggle's Product Classification Challenge. I had a write-up about the solution in my blog. The final model uses an ensemble of two levels by stacking. 30 runs can get 0.4192 on private LB (top 5%). Training data for level 1 and level 2 was roughly split into 1:1 and one can fine tune this to get a better result.

This competition attracted me because:

• Lots of competitors and lots of solutions in the forum to learn from.
• Feature engineering was quite limited, thus I could focus on trying different models.
• A good chance to use model ensembling.

Dependencies

• CUDA Toolkit 7.0
• Python 2.7.6
  • Lasagne 0.1.dev
  • nolearn 0.5
  • numpy 1.8.2
  • pandas 0.13.1
  • scikit-learn 0.16.1
  • scipy 0.13.3
  • Theano 0.7.0
  • xgboost 0.4

Run

• Put data into the data dir
• run python ensembler.py