Data collection and labeling

How this question breaks down

1. 1Implicit labelsCore19 minClicks, dwell, messages and purchases are free labels logged by the millions, but each one answers a slightly different question and every one of them is bent by where the item was shown.
2. 2Human labelsCore20 minPaying people to label is slow and costly, so the craft is in the guideline, the number of labels per item, measuring agreement beyond chance, and letting the model choose what gets labeled next.
3. 3Noisy labelsCore17 minSome of your labels are wrong; find out which kind of wrong, how much it moves your metrics, and whether to clean, down-weight or train through it.
4. 4Weak supervisionCore29 minWhen there are no labels yet, write rules and borrow signals that are each a bit wrong, estimate how wrong without ground truth, and train a model that generalises past all of them.

Work through the parts in order and you’ve covered the full design. Each part is one topic.