Too many people are taking the approach of just fitting as many models as possible to data and then selecting the best fitting model. This is WRONG! Understanding why your model works is very important especially in areas such as finance where being wrong as bad consequences (loss of money or other assets).
Today I am going to explain the differences between decision trees and linear regression. Understanding their strengths and weaknesses will help you logically explain why one method is working better than the other. Two main considerations is how the model fits the data and how robust that method is given the problem you are trying to solve.
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