
Traps in sampling negative events
When obtaining information from our clients, we often receive access to data consisting only of positive events, e.g. a list of items purchased by each user or clicked ads. Many machine learning models need not only positive but also negative events to be able to correctly estimate the probability of a positive event. These could be items not bought by a user during his visit in the store (despite having a chance to buy them) or ads that the user saw but did not click on. In some projects, there are so many negative events that processing all of them