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README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+- advertising
+- conversion-prediction
+- criteo
+- tabular
+pretty_name: "ads-ml-lab — Week 03: Calibration"
+---
+
+# ads-ml-lab — Week 03: Calibration
+
+The data for week 03 of [ads-ml-lab](https://github.com/o0aditya0o/ads-ml-lab), a
+twelve-week course that rebuilds the core model classes of ads measurement from scratch on
+public data. This repo exists so the leaderboard can serve downloads from a CDN instead of
+from a small VM.
+
+**This is a derived dataset, not a copy.** It is built by
+[`tools/prepare_data.py`](https://github.com/o0aditya0o/ads-ml-lab/blob/main/judge/prepare_data.py)
+from [`criteo/criteo-attribution-dataset`](https://huggingface.co/datasets/criteo/criteo-attribution-dataset).
+
+## The task
+
+Predict, per impression, the probability that the user converted within 30 days.
+
+`conversion` is an **impression-level** label meaning "the user who saw this impression
+converted within 30 days" — not "this impression caused the conversion". Every impression
+in a converting journey carries a 1. It is a legitimate CVR target; it is not attribution.
+
+## Files
+
+| file | rows | contents |
+|---|---|---|
+| `train.csv.gz` | 1,500,000 | features **and** the `conversion` label |
+| `test.csv.gz` | 400,000 | features only |
+| `sample_submission.csv.gz` | 400,000 | required submission format, filled with the train base rate |
+
+`impression_id` is assigned during preparation and has no meaning upstream. It is the join
+key for scoring.
+
+## How it was built
+
+- **Time-ordered split.** The boundary is the 70th percentile of `timestamp`, taken
+  **before** subsampling. Subsampling first would let a test row predate a train row. Train
+  is strictly earlier than test; there is no overlap and no shuffling. A random split on
+  this data inflates AUC by an amount that looks like progress.
+- **Subsampled** to 1,500,000 train and 400,000 test rows so a submission is a
+  few MB rather than fifty.
+- **Leaky columns removed from the test file:** `conversion`, `conversion_timestamp`,
+  `conversion_id`, `cpo`, and `attribution`. That last one is the subtle one — it is
+  Criteo's own last-click flag and is nonzero only on converting journeys, so shipping it
+  would hand over the label under a different name.
+- The held-out labels are **not** in this repo.
+
+## Evaluation
+
+The leaderboard ranks on **normalised entropy** (log-loss relative to a base-rate
+predictor), not AUC. AUC is invariant to monotone rescaling, so a model calibrated three
+times too high scores identically on AUC and would overbid every auction threefold. NE is a
+proper scoring rule and sees both ranking and calibration.
+
+Base rate is about 4.9%, and it drifts slightly downward across the window — the test half
+converts a little less often than the train half. That drift is part of the problem.
+
+## Licence and attribution
+
+CC-BY-NC-SA 4.0, inherited from the upstream dataset. Non-commercial use only, and derived
+works must carry the same licence.
+
+```bibtex
+@inproceedings{DiemertMeynet2017,
+  author    = {{Diemert Eustache, Meynet Julien} and Galland, Pierre and Lefortier, Damien},
+  title     = {Attribution Modeling Increases Efficiency of Bidding in Display Advertising},
+  booktitle = {Proceedings of the AdKDD and TargetAd Workshop, KDD, Halifax, NS, Canada, August, 14, 2017},
+  year      = {2017},
+  publisher = {ACM}
+}
+```