boosting_Adaboost.py

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Load the Iris dataset as an example
iris = load_iris()
X, y = iris.data, iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a base classifier (a simple decision tree)
base_classifier = DecisionTreeClassifier(max_depth=1)

# Create an AdaBoost classifier using the base classifier
adaboost_classifier = AdaBoostClassifier(base_classifier, n_estimators=50, random_state=42)

# Fit the classifier to the training data
adaboost_classifier.fit(X_train, y_train)

# Make predictions on the test data
y_pred = adaboost_classifier.predict(X_test)

# Evaluate the accuracy of the AdaBoost classifier
accuracy = accuracy_score(y_test, y_pred)
print(f"AdaBoost Accuracy: {accuracy}")