Time Aggregation Features for XGBoost Models
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917205046722560 |
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| author | Pinchuk, Mykola |
| author_facet | Pinchuk, Mykola |
| contents | This paper studies time aggregation features for XGBoost models in click-through rate prediction. The setting is the Avazu click-through rate prediction dataset with strict out-of-time splits and a no-lookahead feature constraint. Features for hour H use only impressions from hours strictly before H. This paper compares a strong time-aware target encoding baseline to models augmented with entity history time aggregation under several window designs. Across two rolling-tail folds on a deterministic ten percent sample, a trailing window specification improves ROC AUC by about 0.0066 to 0.0082 and PR AUC by about 0.0084 to 0.0094 relative to target encoding alone. Within the time aggregation design grid, event count windows provide the only consistent improvement over trailing windows, and the gain is small. Gap windows and bucketized windows underperform simple trailing windows in this dataset and protocol. These results support a practical default of trailing windows, with an optional event count window when marginal ROC AUC gains matter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10019 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Time Aggregation Features for XGBoost Models Pinchuk, Mykola Machine Learning 68T05 (Primary) 62H30, 62M10 (Secondary) I.2.6; H.2.8 This paper studies time aggregation features for XGBoost models in click-through rate prediction. The setting is the Avazu click-through rate prediction dataset with strict out-of-time splits and a no-lookahead feature constraint. Features for hour H use only impressions from hours strictly before H. This paper compares a strong time-aware target encoding baseline to models augmented with entity history time aggregation under several window designs. Across two rolling-tail folds on a deterministic ten percent sample, a trailing window specification improves ROC AUC by about 0.0066 to 0.0082 and PR AUC by about 0.0084 to 0.0094 relative to target encoding alone. Within the time aggregation design grid, event count windows provide the only consistent improvement over trailing windows, and the gain is small. Gap windows and bucketized windows underperform simple trailing windows in this dataset and protocol. These results support a practical default of trailing windows, with an optional event count window when marginal ROC AUC gains matter. |
| title | Time Aggregation Features for XGBoost Models |
| topic | Machine Learning 68T05 (Primary) 62H30, 62M10 (Secondary) I.2.6; H.2.8 |
| url | https://arxiv.org/abs/2601.10019 |