The problem is a binary classification one. My dataset contains users with activity over multiple days, where they all start with class 0 and can become class 1 after a certain activity (which is not part of the input features). If I have 1000 class 0 users and 100 class 1 users, I will have 1100 training instances.
What I want to do is expand the data such that for each day of activity of each user, there is a row in the training set. So user 1 with 10 days of activity will have 10 rows in the training data, all with class 0, instead of 1 row. User 2 who has 10 days at class 0 and 5 days at class 1 will have 15 rows instead of 1.
This can give the model multiple times more examples to learn from. The only drawback I see is that it will change the ratio of class 0 to class 1 examples (not sure if that is a problem). Are there any unforeseeable disadvantages to this approach?