---
license: cc-by-nc-sa-4.0
tags:
- advertising
- conversion-prediction
- criteo
- tabular
pretty_name: "ads-ml-lab — Week 01: CVR baseline"
---

# ads-ml-lab — Week 01: CVR baseline

The data for week 01 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}
}
```