bagging and boosting

from sklearn.datasets import load_wine
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

# Load the Wine dataset
wine = load_wine()
X, y = wine.data, wine.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Initialize a Decision Tree Classifier
dt = DecisionTreeClassifier(random_state=42)

# Initialize a Bagging Classifier
bagging = BaggingClassifier(dt, n_estimators=10, random_state=42)

# Train the Bagging Classifier
bagging.fit(X_train, y_train)

# Evaluate the performance of the Bagging Classifier
print("Accuracy:", bagging.score(X_test, y_test))