from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

# Load the iris dataset
iris = load_iris()

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)

# Create a Decision Tree Classifier
clf = DecisionTreeClassifier()

# Train the model using the training data
clf.fit(X_train, y_train)

# Use the score() method to evaluate the performance of the model on the testing data
score = clf.score(X_test, y_test)

print("Model accuracy: {:.2f}".format(score))