Suppose we build a classification tree based on a sample of 1000 observations, and continue splitting the tree until there are 1000 terminal nodes. In other words, we continue making splits until there is only one observation from the training sample in each of the terminal nodes. Which ONE of the statements below is valid? A classification tree constructed in this way would have perfect accuracy, sensitivity, and specificity if we estimate these quantities based on new observations which were not used for training. While the accuracy of a classification tree constructed in this way would most likely be quite good, we can't comment on what the sensitivity and specificity would be. A classification tree constructed in this way is very useful for making predictions because it has so many terminal nodes. A classification tree constructed in this way is very likely overfitting the training data