I have a multi label - multi class classifier that aims to predict the top 3 selling products out of 11 possible for a given day.
Using scikit learn's OneVSRest with XgBoost as an estimator, the model gets a hamming loss of 0.25.
Im not familiar with HL, I have mainly done binary classification with roc_auc in the past.
Is this an okay score and how can I describe the effectiveness of the model? does it mean that the model predicts 0,25 * 11 = 2,75 labels wrong on average?