bagging boosting

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.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 Random Forest Classifier
rf = RandomForestClassifier(n_estimators=10, random_state=42)

# Train the Random Forest Classifier
rf.fit(X_train, y_train)

# Evaluate the performance of the Random Forest Classifier
print("Accuracy:", rf.score(X_test, y_test))