# Optimal choice: new machine learning problem and its solution

## TL;DR

This paper introduces the optimal choice problem in machine learning, formalizes it, discusses theoretical challenges, and proposes two practical solutions that perform well on real-world data.

## Contribution

It formalizes a new supervised learning problem, analyzes its theoretical properties, and offers two effective algorithms for practical solutions.

## Key findings

- Both proposed methods achieve good results on real-life data.
- The problem does not meet traditional statistical learning assumptions.
- Efficient solutions are possible despite theoretical challenges.

## Abstract

The task of learning to pick a single preferred example out a finite set of examples, an "optimal choice problem", is a supervised machine learning problem with complex, structured input. Problems of optimal choice emerge often in various practical applications. We formalize the problem, show that it does not satisfy the assumptions of statistical learning theory, yet it can be solved efficiently in some cases. We propose two approaches to solve the problem. Both of them reach good solutions on real life data from a signal processing application.