I have a model that I train on same data, but i want a feature to have a stronger weight.
Say I have three features:
Car manufacturer's name Price Top speed
and I want to classify my cars as "Sport", "Luxury", "Family", "Compact" using these indicators. However, say that I realise the manufacturer's name is the strongest indicator, so I want that to weigh more. For example, if I buy a Ferrari, that is a sport car, even though the price tag may make it look like a luxury car. So, while I want to use all features, I want one of them to have a stronger weight (this is just a silly example, my actual problem is different, but this gives an idea). If a manufacturer makes all 4 kinds, speed and price are important, otherwise the name should have a stronger weight than the price and top speed. Basically, my tree should really look at the manufacturer's name and then split depending on the other features.
Can I set up sklearn in python to do this? How?