how does splitting at a node occur in a decision-tree with non-categorical data?

Question

According to a website (:http://dataaspirant.com/2017/01/30/how-decision-tree-algorithm-works/) , these values are chosen randomly for both gini index and Entropy method:

I don't think this is the case with any optimized way of creating a decision tree. In this image(different example) the value is 2.45 for the root node:

Was this value chosen randomly like explained in the website? If not and the value is not chosen randomly then how is it calculated?

Answer 1

In principle, it considers all possible splits, ordering the samples by the feature value and calculating the gini index (or other criterion) improvement for each split. It then chooses the cutoff value to achieve the split in the ordered samples.

In practice, some optimization occurs, ignoring constant features, using approximated criterion improvements, etc.

https://github.com/scikit-learn/scikit-learn/blob/7389dbac82d362f296dc2746f10e43ffa1615660/sklearn/tree/_splitter.pyx#L304