Multiclass classification problem with multiple targets to be predicted We have a supervised multi-class classification problem where we need to predict two targets for each sample: 'brand' and 'category'. Our features are 'shop_name' which can be any proper noun and several categorical features ('shop_type', 'building_type').
In the training data, 'brand' is not always present, but 'category' is always present. When brand is present, the category is directly dependent on it. For e.g. If the brand is 'Snickers', the category is already known to be 'Foodstuff'. In other cases, the category has to be determined from the input features.
Here is a representative example of our training data:




shop_name
shop_type
building_type
brand
category




Snickers Ltd

huge
Snickers
Foodstuff


Acme Intl
office stationary
medium

Office Equipment


Davidoff cigars
cigarette
big
Davidoff
Smoking


Sample Company
car repair
small

Automobile




Features: shop_name (free text), shop_type (categorical - 100 types), building_type (categorical - 20 types)
Target: brand (categorical - 2500 brands), category (categorical - 250 categories)
How should we structure our classification algorithm to determine brand and category? We have a well known list of about 2500 brands that we are interested in.
You might want to visit the definition of multi-class and multi-label problem. In this case looks like are aiming to solve a multi-label problem. Refer to the following problem, this might help you better understand your situation.  https://stats.stackexchange.com/a/11866/286349