Stochastic Gradient Trees - Python

Python Version 3.7 or later

Used Python libraries:

Usage:

 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)

Binary classification example:

python classification_breast.py

Multiclass classification (using the One-vs-the-rest multiclass strategy):

python classification_iris.py

Regression example:

python regression_diabetes.py