Will the MAE of testing data always be higher than MAE of training data? On the Kaggle Course Page the chart below shows that MAE of testing data is always higher than MAE of training data. Why is this the case? Is it only limited to DecisionTreeRegressor model? Or the graph is wrong and in practice the MAE of testing can be lower than MAE of training?
Test MAE of a model can be lower than Train MAE when it falls under below cases:


It is possible when you have not sampled the data or split the test train data perfectly.
It is possible when your test data is small and its not a good representative of train data, then there may or may not be a case when for that test data it behaves good and gives low error.
There can be a case when you use regularization and your train data move away from overfitting i.e. increased train error but generalizing good on test data i.e. low train error.