# Building a binary classifier on uncertain 0's

When building models to predict probability of sales etc. Its intuitive to select customers who already have bought the product as training data for class 1 and customers who does not have the product as class 0.

But, many of the customers who does not have that product may have never been introduced to it, and would have bought it if they were. So they don't really belong in the 0 class in the training data.

Doe anyone have any thoughts/reflections on this? Whats the consequence?, can we do something to mitigate etc.

• Can you classify them into "not bought" and "don't know" or something similar? As in do you have this information? If not then put them all in 0 as "other". – user2974951 Jan 16 '19 at 8:19
• unfortunatly, the data quality is not that good. So there is no way to seperate. – Rob Jan 16 '19 at 8:39
• Then there is not much to be done. – user2974951 Jan 16 '19 at 8:53

In binary classification, if you can't detect a given input for class 1, it is in class 0. Hence, you just need to consider is the input in class 1 which you have their properties and rich training data. Therefore, if an input data does not classify as a member of class 1, it would be in class 0.