How to train a model when instead of a target we have a range where it is?

Question

Often in machine learning we have a situation when target is numeric (real or integer). Each target comes with an associated input vector. The goal is to learn the mapping from the input vectors to the target. For example:

(1.2, 'A', 3) -> 4.0
(3.2, 'C', 2) -> 1.0
...
(0.8, 'A', 2) -> 5.0
(5.7, 'B', 7) -> 1.0

However, in some cases we do not know the target. We only know that is larger or smaller that a certain value. For example:

(1.2, 'A', 3) -> >3.0
(3.2, 'C', 2) -> =1.0
...
(0.8, 'A', 2) -> >3.0
(5.7, 'B', 7) -> =1.0

In the above example, we know that for the first vector the target is larger than 3.0, but we do not know what exactly it is.

How should one consider the above described situation? Are there standard method to do it?

Answer 1

Have a look at survival analysis, which deals with events of the form you describe. In particular left, right, and interval censored data & regression (https://www.quantics.co.uk/blog/introduction-survival-analysis-clinical-trials/).

Code is available here https://github.com/liupei101/TFDeepSurv And https://adamhaber.github.io/post/survival-analysis/

Answer 2

Based on comments, I think that you should write some code, which: a) encode target values, as network-friendly variables b) decode the output of network to parameters Gaussian-like function.