[scikit-learn] obtaining intervals from the decision tree struture

[Sole Galli]

Hello,

I would like to obtain final intervals from the decision tree structure. I am not interested in every node, just the limits that take a sample to a final decision /leaf.

For example, if the tree structure is this one:

|--- feature_0 <= 0.08
|   |--- class: 0
|--- feature_0 >  0.08
|   |--- feature_0 <= 8.50
|   |   |--- feature_0 <= 1.50
|   |   |   |--- class: 1
|   |   |--- feature_0 >  1.50
|   |   |   |--- class: 1
|   |--- feature_0 >  8.50
|   |   |--- feature_0 <= 60.25
|   |   |   |--- class: 0
|   |   |--- feature_0 >  60.25
|   |   |   |--- class: 0

Then, I would like to obtain these limits:

0-0.08 ; 0.08-1.50; 1.50-8.50 ; 8.50-60; >60

Potentially as the following numpy array:

[-np.inf, 0.08, 1.5, 8.5, 60, np.inf]

Is it possible?

I have a stackoverflow question here for more details and code
https://stackoverflow.com/questions/75663472/how-to-obtain-the-interval-limits-from-a-decision-tree-with-scikit-learn

Thank you!
Sole

Sent with [Proton Mail](https://proton.me/) secure email.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://mail.python.org/pipermail/scikit-learn/attachments/20230307/36b49e96/attachment.html>