For problem sets that are given over purely nominal feature sets, classification methods based on vector spaces are not always the best solution. I'm curious as to how strongly you agree or disagree with this statement. If you were doing ML classification on nominal data, which approach would you use? Provide detailed explanations for your answers to the two questions posed.

What does the pseudocode for this algorithm in the image attached mean in plain English? (Algorithm 2 — the image attached came from a Computer Science whitepaper with keywords: [Exact] Dynamic Programming, Optimal Decision Trees, Reinforcement Learning, Artificial Intelligence, Interpretability). Please explain with either Python Java code if possible (or any programming language). Also, where can I find a reference that explains all the notation used in this pseudocode found in the image? I see this type of notation frequently but can’t find a key or a reference that explains what type of symbols these all are (except for a few of them which I know to be math symbols). Much of this notation I can’t find a reference to explain what it means anywhere, please advise and thanks in advance.

Vector space-based classification methods may not be ideal when the problem set just has nominal features due to fewer variables. Your thoughts? Machine learning to categorize nominal data?

Generate mathematical model generate for FLP using Linear Assignment Problem! What methods/algorithms you should use to solve this problem?