Solved – How Gradient boosting can be more interpretable than CART

I found this document which compare some learning methods and I don't understand this table :
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Gradient boosting has a better intepratability score than CART.
How is it possible ? I thought gradient boosting was an ensemble method, so we have to take a look at all the trees in order to understand how the classification is built.

Dou you think there is a mistake here ?

Im sure this is a typo. This document appears to be lecture notes from from Dr. Hastie from Stanford. Please look at Dr. hastie's book by following the link below at pg 351 table 10.1 has an accurate comparison and comprehensive background for machine learning methods, but does not have gradiant boosting method compared.

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