Public Machine Learning Solver Framework for Novices in the Machine Learning Domain

2026-06-06

AI Summary

This work addresses the challenges non-expert users face in selecting appropriate machine learning algorithms and constructing effective workflows. To lower the barrier to ML adoption, the authors propose a semi-automated recommendation platform that integrates automated data profiling—capturing characteristics such as class imbalance and missing values—with first-order logic reasoning, transfer learning, and crowdsourced expert knowledge. For the first time, this system delivers transparent, structured recommendations for complete ML pipelines. It ranks suggestions by relevance and features an extensible architecture that continuously incorporates new algorithms and domain-specific insights. The platform is deployed as a free online service, significantly democratizing access to machine learning for practitioners without specialized expertise.

Abstract

Solving machine learning problems is complex and typically reserved for experts. Over the past two decades, systems have emerged to support non-experts. Based on our review, we identify three categories: (1) fully automated AutoML systems, (2) expert cheat sheets for algorithm selection, and (3) decision-support systems using selection criteria (accuracy, transparency, data requirements). We propose a new platform combining categories 2 and 3 to deliver semi-automated, intelligent solution recommendations for non-experts. Unlike existing approaches that recommend a single algorithm, our platform suggests a complete pipeline tailored to the user's problem. It integrates expert-defined selection criteria with transfer learning and automatically extracts data characteristics (e.g., class imbalance, missing values) from user-provided datasets. The platform uses first-order logic to reason over its knowledge base and recommends suitable algorithms ranked by relevance. It features a user-friendly interface and connects to a crowdsourcing platform for ML experts, ensuring continuous updates. The platform is built incrementally, allowing seamless integration of new algorithms, criteria, and domain knowledge. To our knowledge, this is the first free, publicly accessible online framework that systematically captures and operationalizes expert knowledge to guide non-experts in solving ML problems in a structured, transparent manner.

Problem

• Machine Learning
• Non-experts
• Problem Solving
• Expert Knowledge
• Decision Support

Innovation

• semi-automated ML
• expert knowledge integration
• pipeline recommendation
• first-order logic reasoning
• crowdsourced expert feedback