CART: stopping criteria

[Pilar O]

I have two questions about the CART algorithm and the information in the video:

Q1: What is the stopping criteria to decide to not continue creating branches and asking questions and dividing the data?

Q2: What does the “regression” part of the name stand for?

[Simon]

A1: various stopping criteria can be used, such as

– the amount of data is too small (below some manually-chosen threshold) for us to reliably choose a good question to make a split

– all possible splits would result in one of the branches have too few data points (below some manually-chosen threshold)

– none of the possible splits results in a sufficiently large reduction in entropy (again, we set a threshold by hand)

– all data points have the same value for the predictee (i.e., we have perfectly predicted its value for all data points)

A2: regression refers to the case where the predictee is a continuous numerical value; classification refers to the case in the video, where the predictee is discrete

[Author]

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