I'm new to machine learning so I apologize in advance if the answer is obvious.
I have a dataset that includes an address feature. I was hoping to use one hot encoding to create a feature for each street that appears in the dataset. So if my dataset looked like:
Miles| Address 4 | 111 First St 2 | 456 Grenada Ave 9 | 789 Grenada Ave
It would become:
Miles| Address | First_St | Grenada_Ave 4 | 111 First St | 1 | 0 2 | 456 Grenada Ave | 0 | 1 9 | 789 Grenada Ave | 0 | 1
Doing this blows up the number of features out to over 1500 and puts both the train and test files from around 90 mb to over 5gb.
I was hoping to apply a RandomForest to these new datasets, but when I call
forest.fit (using Python/sklearn) the memory usage balloons out to 60gb and then the Python process dies.
Couple of questions:
Is it considered bad practice to create this many features? If not - how does one go about modeling with data this size?