from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
# Load data
X, y = load_iris(return_X_y=True)
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Train model
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
# Predict and print accuracy
accuracy = clf.score(X_test, y_test)
print("Classification Accuracy:", accuracy)
# Create data
X, y = make_regression(n_samples=100, n_features=1, noise=5)
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Train model
reg = DecisionTreeRegressor()
reg.fit(X_train, y_train)
# Predict and print score
score = reg.score(X_test, y_test)
print("Regression R^2 Score:", score)
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