T$2$ will fit the training set no better than T$1,$ since T$1$ is the tree with the minimumclassifier error on the training set, and some information is lost in the pruning of T$1$ to create T$2.$However, there is no guarantee on the test error of T$1$ being smaller or greater than T$2($eithercase can happen$)$