Stochastic Gradient Trees1 by Henry Gouk, Bernhard Pfahringer, and Eibe Frank implementation in Python. Based on the parer's accompanied repository code.
- numpy>=1.20.2
- scipy>=1.6.2
- pandas>=1.3.3
- scikit-learn>=0.24.2
from pysgt.StochasticGradientTree import StochasticGradientTreeClassifier from sklearn.model_selection import train_test_split from sklearn.datasets import load_breast_cancer from sklearn.metrics import confusion_matrix, accuracy_score, log_loss def train(X, y): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.34) tree = StochasticGradientTreeClassifier() tree.fit(X_train, y_train) y_pred = tree.predict(X_test) proba = tree.predict_proba(X_test) acc_test = accuracy_score(y_test, y_pred) print(confusion_matrix(y_test, y_pred)) print('Acc test: ', acc_test) print('Cross entropy loss: ', log_loss(y_test, proba)) return tree, acc_test if __name__ == "__main__": breast = load_breast_cancer(as_frame=True) X = breast.frame.copy() y = breast.frame.target X.drop(['target'], axis=1, inplace=True) tree, _ = train(X, y)python classification_breast.py Multiclass classification (using the One-vs-the-rest multiclass strategy):
python classification_iris.py python regression_diabetes.py Footnotes
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Gouk, H., Pfahringer, B., and Frank, E. Stochastic gradient trees. In Proceedings of The Eleventh Asian Conference on Machine Learning, volume 101 of Proceedings of Machine Learning Research, pp. 1094–1109. PMLR, 2019. ↩