I have the following problem: I have three classes/modes, let's call them car, bike, and walking. For any given test data instance with some environmental variables such as distance, road quality etc, I would like to predict the cheapest mode. In all instances in the training and test data set, each of the modes is associated with a cost. I would like to use a decision tree due to it being easy-to-understand. Is it possible to change the "performance metric" during training such that the final tree minimizes the cost deviation due to wrongful classification rather than maximizing the accuracy?
I know that the goal of minimizing the cost deviation can be achieved by employing three (linear) regression models, and then choosing the cheapest mode, but I would like to keep the "easy-to-understand" property of decision trees. Also, some of the explaining variables are non-linear (for example, walking is preferred, if it is neither too hot nor too cold).
Is this possible in general?