Hi, to the best of my knowledge if I recall in binary classification tasks like the one described, fitcecoc (Error-Correcting Output Codes) and fitcsvm (Support Vector Machine) operate differently. fitcecoc uses a strategy that decomposes the multi-class problem into multiple binary classification subproblems, which can enhance the model's ability to capture complex decision boundaries. On the other hand, fitcsvm directly builds a binary classifier without this decomposition.
The superior performance of fitcecoc in this scenario could be attributed to its ability to handle intricate relationships between classes more effectively by breaking down the problem into simpler binary decisions. This decomposition strategy can lead to better generalization and improved accuracy, especially when dealing with complex data distributions or overlapping classes.
Hope this answers your question.
