I'm new to machine learning, and I'm trying to get a sense of how you optimize data for a model. I'm following this official Kaggle tutorial, which teaches the basics of machine learning through house price prediction. They use a decision tree, but I found it odd which features they feed into the model to predict the price of a house:
house_price_features = ['Rooms', 'Bathroom', 'Landsize', 'Latitude', 'Longitude']
Rooms, bathrooms, and landsize all make sense to me - but latitude and longitude? Obviously there is a correlation between location and price, but it's not going to follow a nice curve. Sometimes, going a block up will increase house prices twofold; sometimes, it'll have no effect at all. Intuitively, I feel like all a model can do with those features in predicting price is overfit. So, my question is twofold:
- Were they right in giving this model latitude and longitude to predict price, or is this extraneous information that can only hurt the model? Why?
- If the answer to the above is "no", is there any transformation of the latitude and longitude data (i.e. into neighborhood IDs) that would make the data more helpful?