An important aspect of practical classifiers is
interpretability. Learning compact but highly accurate models that
help in human decision-making is challenging. Most such simple scoring
systems were constructed by human experts using some heuristics and
are not optimal. In many prediction tasks such as medical diagnostics,
there are many more challenges: finding optimal individual treatment;
taking budget into consideration, and the budget (any finite resource
such as time, money, or side effects of medications) in real-life
applications is always limited. I will consider principled methods to
learn interpretable simple rules purely from data. I will also show
possible solutions to take the limited budget into account, and
discuss some perspectives for development of methods of personalised
medicine.