Doctorado-ML/stree
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README.md
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# STree
![CI](https://github.com/Doctorado-ML/STree/workflows/CI/badge.svg) ![CI](https://github.com/Doctorado-ML/STree/workflows/CI/badge.svg)
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![https://img.shields.io/badge/python-3.11%2B-blue](https://img.shields.io/badge/python-3.11%2B-brightgreen) ![https://img.shields.io/badge/python-3.11%2B-blue](https://img.shields.io/badge/python-3.11%2B-brightgreen)
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# STree ![Stree](https://raw.github.com/doctorado-ml/stree/master/example.png)
Oblique Tree classifier based on SVM nodes. The nodes are built and splitted with sklearn SVC models. Stree is a sklearn estimator and can be integrated in pipelines, grid searches, etc. Oblique Tree classifier based on SVM nodes. The nodes are built and splitted with sklearn SVC models. Stree is a sklearn estimator and can be integrated in pipelines, grid searches, etc.
![Stree](https://raw.github.com/doctorado-ml/stree/master/example.png)
## Installation ## Installation

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stree/Splitter.py
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Train time - True / Test time - False Train time - True / Test time - False
""" """
# data contains the distances of every sample to every class hyperplane # data contains the distances of every sample to every class hyperplane
# array of (m, nc) nc = # classes # array of (m, nc) nc = k if ovr, nc = k*(k-1)/2 if ovo
data = self._distances(node, samples) data = self._distances(node, samples)
if data.shape[0] < self._min_samples_split: if data.shape[0] < self._min_samples_split:
# there aren't enough samples to split # there aren't enough samples to split