🌳 Oblique Decision Trees in Python
A python interface to oblique decision tree implementations:
- OC1 (Murthy, Kasif, Salzberg 1994)
- CART-Linear Combinations (Breiman et al, 1984, Chapter 5)
Installation (Python 3)
First install numpy with:
pip install numpy
then run:
pip install git+https://github.com/AndriyMulyar/sklearn-oblique-tree
Use
Trees can be induced with the normal scikit-learn classifier api. For instance:
from sklearn.datasets import load_iris, load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn_oblique_tree.oblique import ObliqueTree
random_state = 2
#see Murthy, et all for details.
#For oblique with consideration of axis parallel
#tree = ObliqueTree(splitter="oc1, axis_parallel", number_of_restarts=20, max_perturbations=5, random_state=random_state)
#
#For multivariate CART select 'cart' splitter
#tree = ObliqueTree(splitter="cart", number_of_restarts=20, max_perturbations=5, random_state=random_state)
#consider only oblique splits
tree = ObliqueTree(splitter="oc1", number_of_restarts=20, max_perturbations=5, random_state=random_state)
X_train, X_test, y_train, y_test = train_test_split(*load_iris(return_X_y=True), test_size=.4, random_state=random_state)
tree.fit(X_train, y_train)
predictions = tree.predict(X_test)
print("Iris Accuracy:",accuracy_score(y_test, predictions))Acknowledgements
VCU Imbalanced Learning and Data Stream Mining Laboratory
