I want to implement my own version of the CART Decision Tree from scrach (to learn how it works) but I have some trouble with the Gini Index, used to express the purity of a dataset.
More precisely, I don't understand how Gini Index is supposed to work in the case of a regression tree.
The few descriptions I could find describe it as :
gini_index = 1 - sum_for_each_class(probability_of_the_class²)
Where probability_of_the_class is just the number of element from a class divided by the total number of elements.
But I can't use this definition in the case of regression where I have continuous variables.
Is there something I misunderstood here ?