I have been working on an ML problem in which I want to predict an interval of money say, a, b, c, d that might be lent to a customer given its credit files, those amounts are represented on ordered bins the i.e a < b < c < d.
First I faced this problem as a multiclass classification problem and even though I did not obtain "good" performance I never thought It could be because of the inherent order on my target.
After googling it I found a paper in which a method to perform classification on ordinal target was developed, but I'm not still sure what are the implications on this scenario and even why it needs special attention.
In that paper is stated:
Standard classification algorithms for nominal classes can be applied to ordinal prediction problems by discarding the ordering information in the class attribute. However, some information is lost when this is done, information that can potentially improve the predictive performance of a classifier.
But it does not clarify the point to me.
Could you please help me to understand the implications of an ordinal target? Might this order, be responsible for poor performance on a multiclass classification task when this order is not considered by applying Logistic Regression, an Ensemble method or any other classification model?