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CatBoost lineage: ordered boosting and leakage-resistant category statistics

Addressed training/test prediction shift by ordering examples so supporting predictions and target statistics exclude the current label, combining practical ordered boosting and categorical-feature processing.

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Addressed training/test prediction shift by ordering examples so supporting predictions and target statistics exclude the current label, combining practical ordered boosting and categorical-feature processing.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

Original2017dynamic-boosting six-person byline differs from2018NeurIPS five-person CatBoost paper; source labels Liudmila Ostroumova Prokhorenkova/Prokhorenkova retained without resolving identity variants. Unbiased refers to specified residual/statistical mechanisms and assumptions, not a universally unbiased final estimator, absence of all leakage or dominance over every dataset.

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People and systems

Anna Veronika Dorogush; Andrey Gulin; Gleb Gusev; Nikita Kazeev; Liudmila Ostroumova Prokhorenkova; Aleksandr Vorobev; Liudmila Prokhorenkova

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

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English catalogue narrative

CatBoost lineage: ordered boosting and leakage-resistant category statistics

Addressed training/test prediction shift by ordering examples so supporting predictions and target statistics exclude the current label, combining practical ordered boosting and categorical-feature processing.

Scientific scope

Original2017dynamic-boosting six-person byline differs from2018NeurIPS five-person CatBoost paper; source labels Liudmila Ostroumova Prokhorenkova/Prokhorenkova retained without resolving identity variants. Unbiased refers to specified residual/statistical mechanisms and assumptions, not a universally unbiased final estimator, absence of all leakage or dominance over every dataset.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Anna Veronika Dorogush; Andrey Gulin; Gleb Gusev; Nikita Kazeev; Liudmila Ostroumova Prokhorenkova; Aleksandr Vorobev; Liudmila Prokhorenkova

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Original sources