mirror of https://github.com/Doctorado-ML/STree.git synced 2025-08-15 15:36:00 +00:00

Go to file

Solve the mistake of min and max distance

The split criteria functions min and max distance return classes while
max_samples return distances positives and negatives to hyperplane of
the class with more samples in node

2020-06-17 00:13:52 +02:00

notebooks

#2 update benchmark notebook

2020-06-15 10:33:51 +02:00

stree

Solve the mistake of min and max distance

2020-06-17 00:13:52 +02:00

.coveragerc

#4 Add code coverage & codacy badge

2020-06-06 03:04:18 +02:00

.gitignore

#4 Add code coverage & codacy badge

2020-06-06 03:04:18 +02:00

codecov.yml

#6 - Update tests and codecov conf

2020-06-11 13:45:24 +02:00

example.png

Implement grapher and notebook to test it

2020-05-22 19:42:13 +02:00

LICENSE

Initial commit

2020-05-09 23:01:30 +02:00

main.py

#2 Add predict and score support

2020-06-14 14:00:21 +02:00

pyproject.toml

#4 Add code coverage & codacy badge

2020-06-06 03:04:18 +02:00

README.md

#3 Add sample_weights to score, update notebooks

2020-06-09 01:46:38 +02:00

requirements.txt

Fix problem in _min_distance

2020-06-12 00:50:25 +02:00

setup.py

Fix problem in _min_distance

2020-06-12 00:50:25 +02:00

README.md

Stree

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.

Installation

pip install git+https://github.com/doctorado-ml/stree

Examples

Jupyter notebooks

Benchmark
Benchmark
Test features
Adaboost
Gridsearch
Test Graphics

Command line

python main.py

Tests

python -m unittest -v stree.tests