Could you please write a brief program about the 2 model predictions below?

## making predictions using the Random Forest algorithm from sklearn.ensemble import RandomForestRegressor forest_model = RandomForestRegressor(n_estimators=100, max_depth=10) forest_model.fit(train_x, train_y ) predicted_random_forest = forest_model.predict(test_x) print("Mean Absolute Error using Random Forest:", mean_absolute_error(test_y, predicted_random_forest)) Mean Absolute Error using Random Forest: 16582.607091473192 ## making predictions using the Decision Tree algorithm from sklearn.tree import DecisionTreeRegressor decision_model = DecisionTreeRegressor() decision_model.fit(train_x, train_y ) predicted_decision_trees = decision_model.predict(test_x) print("Mean Absolute Error using Decision Trees:", mean_absolute_error(test_y, predicted_decision_trees)) Mean Absolute Error using Decision Trees: 26562.54520547945