from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

def prune_tree(tree, X, y):
    return tree
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
clf = DecisionTreeClassifier(random_state=42)
clf.fit(X_train, y_train)
pruned_tree = prune_tree(clf, X_train, y_train)
y_train_pred = pruned_tree.predict(X_train)
train_accuracy = accuracy_score(y_train, y_train_pred)
train_conf_matrix = confusion_matrix(y_train, y_train_pred)
y_test_pred = pruned_tree.predict(X_test)
test_accuracy = accuracy_score(y_test, y_test_pred)
test_conf_matrix = confusion_matrix(y_test, y_test_pred)
print(f"Train Accuracy: {train_accuracy:.2f}")
print("Train Confusion Matrix:")
print(train_conf_matrix)
print(f"\nTest Accuracy: {test_accuracy:.2f}")
print("Test Confusion Matrix:")
print(test_conf_matrix)