Comparación entre árboles de regresión CART y regresión lineal (2013)
Linear regression is the most widely used method in statistics to predict values of continuous variables due to its easy interpretation, but in many situations the suppositions to apply the model are not met and some users tend to force them leading them to erroneous conclusions. CART regression trees is a regression alternative that does not require suppositions on the data to be analyzed and is a method of easy interpretation of results. This work compares predictive levels of linear regression with CART through simulation. In general, it was found that when the correct linear regression model is adjusted to the data, the prediction error of linear regression is always lower than that of CART. It was also found that when linear regression model is erroneously adjusted to the data, the prediction error of CART is lower than that of linear regression only when it has a suﬃciently large amount of data.