This document summarizes the features, models, feature selection, and final ensemble used in an R 5th Private Solution. It describes various statistics and ratio features calculated on the data. It explains that light gradient boosting machines (LGBM) are used with different feature sets and training on whole or subsetted data based on rho values. Feature selection uses permutation importance. The final ensemble averages 4 models for hhb and 3 for other targets, varying the feature sets and whether training was on whole or subsetted data.

1. R 5th Private Solution

2. FEATURES
• Various statistics – mean/max/min/sd of dst (by all values and by
neighbour groups, for example 650-690 or 930-990)
• dstX * srcY / (dstY * srcX) features
• Same as previous but by groups and aggregated with mean and max
• Ratio interactions between original and new generated features
• Some ratio features with corrections if the given dst is NaN (look at
the code)

3. MODELS
• LGBM gbdt
• Loss: ‘fair’
• Different set of features for diversity of final models
• Models trained on whole dataset and models trained only on given rho

4. FEATURE SELECTION
• For different target for different train sets
• Permutation importance as general technique

5. FINAL ENSEMBLE
• For hhb 4 models:
➢ Trained on whole dataset with big set of features
➢ Trained on whole dataset with small set of features
➢ Trained by rho with small set of features
➢ Trained by rho on big set of features
• For hbo2/ca/na the same except 4th model
• Blending coefficients depending on rho

6. EXTRA FILES AND REMARKS
• Extra .csv files contain selected features for every model and are
needed for readable code
• You should place it in the same directory as .R code file
• In data_dir variable (look at the first rows of code) you should put
the path of directory where the files were placed