# Machine learning : learn feature value range for a classification

Which domain the problem belongs to? Given a set of products some are classified as cheap and some not. The task is to determine the price range (probablistic) for cheap products ? Supervised classifiers arrive at a function and can classify a new instance but does not provide the range of feature values. This seems like a pattern recognition problem but I am not able to get which class of problems it belongs to.

• like how to find a pattern saying, all products with price <2000 are generally classified as cheap for 90% of the cases. This would be the output after learning over 100s of product features (say for example). – user439521 Nov 22 '14 at 18:58
• Doesn't the decision depend on the type of product? A \$1000 car would be cheap but a \$1000 toy car would not. – Emre Nov 22 '14 at 21:09
• @Emre yes it does, but for simplicity lets assume all products are from the same category. – user439521 Nov 23 '14 at 5:33
• Why don't you just estimate the conditional (on the class) density of the price then? – Emre Nov 23 '14 at 6:28
• Do you have more than just the price for each item and would an item labeled “cheap” receive that label again if the labeling process were repeated? (I.e. is there a eater agreement problem lumped in with your task?) – Wayne Jul 9 '19 at 14:16