from sklearn.datasets import load_iris
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error

# 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(,, test_size=0.3, random_state=42)

# Train a linear regression model
lin_reg = LinearRegression(), y_train)
lin_reg_preds = lin_reg.predict(X_test)
lin_reg_mse = mean_squared_error(y_test, lin_reg_preds)

# Train a decision tree classifier
dtc = DecisionTreeClassifier(), y_train)
dtc_preds = dtc.predict(X_test)
dtc_accuracy = accuracy_score(y_test, dtc_preds)

print("Linear regression MSE:", lin_reg_mse)
print("Decision tree accuracy:", dtc_accuracy)