# Giving more weight to a particular feature in scikit-learn decision trees

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?

• Not entirely sure but, I think you have to tune the min_weight_fraction_leaf parameter by providing some sample weights. Oct 11 '17 at 12:21
• @RahulAedula Thank you, I tried to read the documentation, but I am quite confused, especially since it looks like you pass in it a single float value.
– user
Oct 12 '17 at 1:32