I am new to Machine Learning and I am trying to undrestand the Out of Bag Error in Random Forests and its use.
Let's say that we have a dataset. First we use the whole dataset (without splitting it) to get a Random Forest and its Out of Bag error. Then we split the dataset, train a Neural Network on the training part and test it on the test part of the dataset.
Can I choose between the two models by comparing the Out of Bag error of the random forest with the total test error of the Neural Network ? Does it make sense ?