It is essentially a choice modelling problem, but hopefully can be addressed by classification.
Suppose one needs to choose a route to drive to work among many candidates in his mind. These candidates have been summarized with certain features e.g. distance, cost, and number of red lights en route.
We have many individuals like him. How do we build a classification model to make predictions based on the route feature?
My thought is to label every chosen route as 1 and not chosen routes as 0. Therefore for each individual, there is a 1 and many 0s. But to each individual, their candidates are not at the same length/having the same impedances.
How do I take this into account? By a scaler such as MinMaxScaler to standardise everyone? or if there is a way to take the individuality of choice sets into account in a classification model?