Four hyperparameters of the GBT ensemble were optimized using cross-validation: tree depth, leaf regularization, border count, and the quantile coverage p. The final hyperparameters of all GBT new sub-models were chosen to be 4, 1, 250, and 0.80, respectively. For the GBT prev model, they were 3, 4, 50, and 0.82, respectively. Therefore, the loss function of the new and prev sub-model responsible for the regression output was . For the new upper and lower quantile models the loss functions were and respectively, while they were and for the prev upper and lower quantile models.
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