I have a large sales dataset and was intending to use a CART tree to predict the sales price of each item depending on lots of input factors such as the sales region etc. To achieve this I used the rpart package in R. I calculated the tree and pruned it according to this website which means pruning it back to the level of the smallest 10-fold cross validation error.
I tested the predictive power of the tree on unknown data (randomly splitting my dataset 80:20 over and over). The results confuse me. I calculated the MAPE of the predictions on the test set for both the pruned and the unpruned model. Contrary to my expectations the MAPE for the pruned tree is 0.379 while the MAPE for the unpruned tree is 0.233. For my dataset the difference between those two numbers is fairly large. I was expecting the pruned tree to perform better than the unpruned tree on unknown data.
Is this a common thing to happen? What are possible reasons?