3
$\begingroup$

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 ?

$\endgroup$

1 Answer 1

2
$\begingroup$

In regression trees, sum of squared error (SSE) is the criterion for tree split. The first split is based on the feature/predictor and its values in your training set that yields the lowest SSE value. And then so on for the further splits.

$\endgroup$
2
  • $\begingroup$ So in the case I have a dataset that contains both continuous and categorical data, I should use a different criterion for tree split depending of the variable type that is tested ? $\endgroup$
    – Nakeuh
    Jul 18, 2018 at 15:03
  • 3
    $\begingroup$ The criteria depends on target variable, not the predictors $\endgroup$
    – Srikrishna
    Jul 18, 2018 at 15:09

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.