kaggle-Otto

12th solution for the Otto Group Product Classification Challenge on Kaggle.

Classifier algorithms

• NN3: Neural Networks with 3 hidden layers
• NN4: Neural Networks with 4 hidden layers. The first is sparse. The link is defined by RI or TRI.
• XGB: XGBoost

Feature extraction methods

• NewX = X * R, X:(N, 93), R:(93, m)
  • Random Indexing(RI): RI controls # of nonzero elements in each row of R.
  • Transposed Random Indexing(TRI): TRI controls # of nonzero elements in each column of R.
• Count Feature(CF): The count of each feature value

5 types of models

• TRI(k_max=4) + NN3
• TRI(k_max=5) + NN3
• NN4(RI)
• XGB + CF
• XGB + RI

Software

• Ubuntu 14.04 LTS
• Cuda 6.5
• python 2.7
• numpy 1.8.2
• scipy 0.13.3
• scikit-learn 0.16.1
• theano 0.7
• pylearn2 0.1dev
• xgboost-0.3

Usage

• Put all *.py files into the folder containing train.csv, test.csv, sampleSubmission.csv
• python otto_nn.py
  • Output files: pred_TRI_kmax_4.npy, pred_TRI_kmax_5.npy, pred_Sparse_RI.npy
• python otto_xgb.py
  • Output files: pred_XGB_CF.npy, pred_XGB_RI.npy
• python make_submission.py
  • Output file: pred005.csv