I read this question Gini Impurity vs Entropy and was wondering why would someone use entropy instead of Gini index in a decision tree with scikit-learn.

Indeed, I find these arguments legit:

Given a choice, I would use the Gini impurity, as it doesn't require me to compute logarithmic functions, which are computationally intensive. The closed form of it's solution can also be found.

And by looking at the following I can't see much a difference between the two graphs:

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