bagging and boosting

from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

#Load the Breast Cancer Wisconsin dataset
cancer = load_breast_cancer()
X, y = cancer.data, cancer.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 an AdaBoost Classifier
boosting = AdaBoostClassifier(dt, n_estimators=50, random_state=42)

#Train the AdaBoost Classifier
boosting.fit(X_train, y_train)

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