Matching Problems to Solutions: An Explainable Way of Solving Machine Learning Problems

TL;DR

This paper introduces a workbench that captures ML problem-solving knowledge, enabling domain experts to match problems with suitable ML algorithms through an explainable, heuristic-based approach.

Contribution

It presents a novel representation of domain and ML problems and a heuristic matching function to assist non-experts in selecting appropriate ML algorithms.

Findings

Proposes a structured representation of ML problem solving knowledge.

Develops a heuristic matching function for algorithm selection.

Outlines validation strategies for the workbench.

Abstract

Domain experts from all fields are called upon, working with data scientists, to explore the use of ML techniques to solve their problems. Starting from a domain problem/question, ML-based problem-solving typically involves three steps: (1) formulating the business problem (problem domain) as a data analysis problem (solution domain), (2) sketching a high-level ML-based solution pattern, given the domain requirements and the properties of the available data, and (3) designing and refining the different components of the solution pattern. There has to be a substantial body of ML problem solving knowledge that ML researchers agree on, and that ML practitioners routinely apply to solve the most common problems. Our work deals with capturing this body of knowledge, and embodying it in a ML problem solving workbench to helps domain specialists who are not ML experts to explore the ML solution space. This paper focuses on: 1) the representation of domain problems, ML problems, and the main ML solution artefacts, and 2) a heuristic matching function that helps identify the ML algorithm family that is most appropriate for the domain problem at hand, given the domain (expert) requirements, and the characteristics of the training data. We review related work and outline our strategy for validating the workbench