Predictive Analytics And Forecasting With Confidence Intervals

via “prediction with confidence intervals and uncertainty quantification”

CatBoost Python Package

Unique: Supports quantile loss functions natively in the training framework, enabling direct optimization of specific quantiles rather than mean predictions. Quantile models are trained with the same symmetric tree structure as standard models, ensuring consistency.

vs others: More straightforward than scikit-learn's quantile regression because CatBoost's quantile loss is integrated into the boosting framework, avoiding the need for separate post-hoc quantile calibration.