Brief paragraph about the ML models predictions? What does MAE using the specific model mean?

: ## 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: 16582.607091478192 : ## 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)) 1 Mean Absolute Error using Decision Trees: 26562.54520547945 : # evaluate a logistic regression model using k-fold cross-validation from numpy import mean from numpy import std from sklearn.datasets import make_classification from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn. linear_model import LogisticRegression from sklearn import svm # prepare the cross-validation procedure KFold (n_splits=10, random_state=1, shuffle=True) # k-fold cv, the training set is split into k smaller sets CV = # create model model = LogisticRegression() model.fit(train_x, train_y) # evaluate model. cross_val_score helper function on the estimator and the dataset. scores = cross_val_score(model, test_x, test_y, scoring='accuracy', cv=cv, n_jobs=-1) # report performance print('Accuracy: %.3f (%.3f)' % (mean(scores), std(scores))) Accuracy: 0.005 (0.011)