I can give another example. Input / Output: 1.7/0, 2/0, 2.2/1 ,3.5/0 ,4/0 ,5/0 ,8/0 ,9.6/0 ,11/1, 13/1, 14/1, 16/1, 18/1, 20/1. There is an error in this dataset: 2.2/1. But you can train a model on this set to predict 2.2/0 (a small / regularized model would do that) . You could also train a model to predict 1 for 2.2, but it would probably be overfitting. The same idea applies to any concept in input and any concept in output.