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