This is one part of data science I've never been able to find good content on: The art of model selection. So, if you have a bunch of data and need to model it, how do you decide which model (i.e. linear regression, XGBoost, neural net, etc.) works? Please elaborate in comments.
the kaggle special: XGboost everything, when it doesn't work just add more trees.
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