In some cases, it is possible to convert a regression problem to a classification problem. For example, the quantity to be predicted could be converted into discrete buckets.
For example, amounts in a continuous range between $0 and $100 could be converted into 2 buckets:
- Class 0: $0 to $49
- Class 1: $50 to $100
This is often called discretization and the resulting output variable is a classification where the labels have an ordered relationship (called ordinal).
In some cases, a classification problem can be converted to a regression problem. For example, a label can be converted into a continuous range.
Some algorithms do this already by predicting a probability for each class that in turn could be scaled to a specific range:
quantity = min + probability * range
Alternately, class values can be ordered and mapped to a continuous range:
- $0 to $49 for Class 1
- $50 to $100 for Class 2
If the class labels in the classification problem do not have a natural ordinal relationship, the conversion from classification to regression may result in surprising or poor performance as the model may learn a false or non-existent mapping from inputs to the continuous output range.