Metalearning is a technique, which enables us to improve classification accuracy in Data Mining. It uses several classifiers to compute final category for a test sample. The most popular metalearning methods are Bagging and Boosting. Effectiveness of these methods with the usage of decision tree (SPRINT) has been tested and presented in this paper.