RF and DT overfitting I am new with Machine Learning and I started with some lessons in Kaggle. There, I learnt how to use DecisionTreeRegressor() and RandomForestRegressor() from sklearn.



However, I cannot really understand how I can verify that my explanatory variables do not overfit the model. For example, the lessons included evaluation with the use of Mean Absolute Error. MAE and MRSE can evaluate whether my Decision Tree depth is optimal or not, but not if my explanatory data are even relevant.



I come from Economics, so I am used to deal with such problems using diagnostics or $R^2$. Is there any equivalent to $R^2$ benchmark to determine whether my explanatory variables are overfitting my model or not?
Yes, and that benchmark is called validation data. The idea is to split your data in some training data, which is what you are going to fit to your models in order to estimate the parameters in them (and make them learn), and some validation data, which is the data you are going to use in order to evaluate your model. 

What you can do is compute the error on your validation data (which the models haven't been trained on) for the different models you want to use, and you would like to keep the model with the lowest validation error.