“If I can use logistic regression for classification problems and linear regression for regression problems, why is there a need to use trees”? Many of us have this question. And, this is a valid one too.
Actually, you can use any algorithm. It is dependent on the type of problem you are solving. Let’s look at some key factors which will help you to decide which algorithm to use:
1) If the relationship between dependent & independent variable is well approximated by a linear model, linear regression will outperform tree based model.
2) If there is a high non-linearity & complex relationship between dependent & independent variables, a tree model will outperform a classical regression method.
3) If you need to build a model which is easy to explain to people, a decision tree model will always do better than a linear model. Decision tree models are even simpler to interpret than linear regression!
Linear, Machine-Learning, Tree — Jul 27, 2018
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