Markov Chains, Computer Science, Programming, Probability, Problem Solving.
train_test_split(X, y, test_size=0.2, random_state=42) # Decision Tree model max_depth = 5 # Adjust the depth as needed decision_tree = DecisionTreeRegressor(max_depth=max_depth) decision_tree.fit(X_train, y_train) # Predictions on the test set y_pred = decision_tree.predict(X_test) # Evaluate the