The models were created using a maximum number of 1000 trees (lower in case early stopping was triggered during cross validation), a learning rate of 0.1, a minimum of 50 datapoints per leaf, a maximum depth of 8, and a subsample of 80% per tree. Feature importance was recorded based on the gain in RMSE decrease during training.