An Issue Tree is step-by-step graphical representation used to break down complex problems into smaller, manageable parts. It’s crucial for structuring and solving problems in AI and ML.


Let’s say you’re trying to clean your messy room but you’re not sure where to start. An Issue Tree is like a roadmap that helps you break down this big task into smaller parts: clean the desk, make the bed, pick up clothes, etc. In AI, it helps us split big tricky problems into smaller chunks that a computer can understand better.

In-depth explanation

In the context of artificial intelligence and machine learning, an Issue Tree (also known as a logic tree) is a hierarchical problem-solving framework that is visual in nature and helps in decomposing complex problems into simpler sub-problems.

An Issue Tree starts with a broad overarching question or issue at its root, the answer or solution to which is unknown. It then branches out into a host of sub-problems or sub-questions, each attempting to clarify or further explain the higher issue. These branches are further divided into sub-nodes, providing a deeper and more detailed examination of the problem. The process continues until the problem is reduced to a level where it can be addressed directly.

For instance, in a machine learning project that aims to improve customer experience, the overarching issue could be: ‘How can we enhance the customer experience?’ This could be split into several different branches, with each representing a particular area of focus such as ‘improving customer service,’ ‘optimizing product portfolio,’ ‘enhancing user interface,’ and so on.

It’s also noteworthy that the strategy for branching may vary. A “MECE” (Mutually Exclusive, Collectively Exhaustive) approach is often adopted to ensure that every sub-category or branch represents a unique part of the core problem, and taken together, they cover all aspects of the problem.

Importantly, an Issue Tree’s structure and layout can significantly simplify the initial stages of model development and can also be beneficial when troubleshooting issues within established models. They provide clarity to the problem-solving process and ensure that all critical aspects of the problem are considered.

Decision Tree, Root Cause Analysis, Problem Framing, MECE Principle