clf = OutputCodeClassifier(LinearSVC(random_state=0), code_size=2, random_state=0)
ECOC basically converts a single k class multi-class problem to (2^(k-1)-1) many binary class classification problems. This is useful because existing state of the art algorithms tend to be more heavily optimised for binary class classification and/or develop bias stemming from over-fitting etc.
OutputCodeClassifier will need to be fed a binary classifier. In the documentation LinearSVC was used but it would be best to try different binary classifiers through cross validation to see which algorithm fits which problem.
code_size mentioned here is the size of the ECOC code matrix which grows at a rate of (2^(k-1)-1). That would be 3 columns for a 3-class problem (which actually is a 1 vs all) and 63 columns for a 6-class problem. We will need to optimize as we go along.