Binary classifier predicting whether an ad impression will be clicked. The dataset is severely imbalanced (~2.5% click rate), so the model uses scale_pos_weight and is evaluated primarily on PR-AUC, not accuracy or ROC-AUC alone.

Full classification report (default 0.5 threshold) and a threshold sweep are saved in eval_metrics.json / the training notebook. Because of the heavy imbalance, consider tuning the decision threshold for your use case (e.g. ranking ads by predicted probability rather than thresholding at 0.5).

Categorical (one-hot): ['ad_category', 'ad_brand', 'target_archetype', 'target_age_range', 'target_gender', 'placement', 'user_archetype', 'user_age_range', 'user_gender', 'day_of_week', 'device_type'] Numeric: ['bid_amount_cents', 'user_session_number', 'hour'] Binary: ['archetype_match']

Note: was_converted was intentionally excluded as a feature since it's a downstream consequence of a click, not known at prediction time.

See the inference / Hugging Face call cells in the training notebook for a full working example.

Part of The Culture ML model suite.