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Sberbank AutoML solution

Dataset preparation

  • If the dataset is big (>2GB) then we calculate features correlation matrix and the delete correlated features
  • Else we make Mean Target Encoding and One Hot Encoding. 
  • After that, we select top-10 features by coefficients of the linear model (Ridge/LogisticRegression)
  • We generate new features by pair division from top-10 features. This method generates 90 new features (10^2–10) and concatenates it to the dataset.

Model training

  • If the dataset is small then we can train three LightGBM models by k-folds, after that blend prediction from every fold.
  • If the dataset is big and the time limit is small (5 minutes) then we just train linear models (logistic regression or ridge)
  • Else we train one big LightGBM (n_estimators=800)

Result

5th place on private leaderboard