underfitting decision trees

from sklearn.tree import DecisionTreeRegressor

# Train a decision tree regressor
tree = DecisionTreeRegressor(max_depth=4, random_state=42)
tree.fit(X_train, y_train)

# Calculate training and testing errors
train_error_tree = mean_squared_error(y_train, tree.predict(X_train))
test_error_tree = mean_squared_error(y_test, tree.predict(X_test))

print(f'Training error (Decision Tree): {train_error_tree:.2f}')
print(f'Testing error (Decision Tree): {test_error_tree:.2f}')