When using out$-$of$-$bag evaluation, each predictor in a bagging ensemble is evaluated using instances that it was not trained on $($because they did

came out in the random sampling$).$ This facilitates a mostly unbiased evaluation of the ensemble without recurring to a separate validation set.

Therefore, there are more instances left available for training and the ensemble can perform a little better.

True

False