is it possible to output more than 2 nodes away from a node in a decision tree? if yes, how to do that with sklearn? usually a decision tree has one root node, some nodes, and some leaves.

lots tutorial illustrate this as something like binary tree.

is it possible more than 2 nodes away from a node in a decision tree?



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by "more than 2 nodes", i mean there are more than 3 splits (in this case, 3, Low, Med, High) away from the root node.

if it is reasonable in real life application, plz provide an open dataset on which a decision tree would spit more than 2 nodes, and a piece of sklearn code.
It is possible to make more than a binary split in a decision tree. Chi-square automatic interaction detection (CHAID) is an algorithm for doing more than binary splits.

However, scikit-learn only supports binary splits for many reasons. One primary reason to limit to just binary splits is that the library can support as many splitting criteria as possible with the same API. For example, Gini Impurity only supports binary splits. 

In practice, only supporting binary splits is not an issue because a series of binary splits can model any number of simultaneous splits.