Let's think of a case of an e-commerce website which lists products for sale. Now a person can come on a particular product page and decide to add it to the shopping cart or not. If we look at it as a classification problem then, specifications of the product can be its features(e.g. for a cell phone, camera resolution, screen size, price, warranty period) and for a particular product page view by a customer either he clicked on add to cart button or not can be the success criteria(classification label).
An observation is that for the same set of features, X for a product page view, very few customers click on add to cart button (y = 1). That means for the same X, there will be many Y=0 and very few Y=1.
My peer says that machine learning algorithms like XGBoost will be able to classify this. While I am not convinced of the fact that how will the algorithm be able to predict that for an X, Y will be 1 when in reality itself it has a very slight chance of being 1 as many customers will not add it to cart.
I know it will give me a probability score of y being 1 but that will be too low to make use of.