Thank you for responding,
What I meant was let us consider just one decision tree for now,
Since every decision made goes till the leaf node and and leaf node contains one data point/observation from training set and every decision made is simply the mean of all the samples in that node therefore in a decision tree every prediction correspondes to a actual value in training set(as each of leaf nodes contain one training sample), so can a single decision tree can be interpreted as simply a function which makes prediction by finding the nearest row in training set
and so it can not predict any value other than values present in training set , right ?
In other words for each row in test set it is finding the best(closest , most representative) possible row in training set ? (Sorry a bit long)