A b s t r a c t . We describe an experimental study of pruning methods for decision tree classifiers when the goal is minimizing l o s s rather than e rror. In addition to two common methods for error minimization, CART's cost-complexity pruning and C4.5's error-based pruning, we study the extension of cost-complexity pruning to loss and one pruning variant based on the Laplace correction. We perform an empirical comparison of these methods and evaluate them with respect to loss. We found that applying the Laplace correction to estimate the probability distributions at the leaves was beneficial to all pruning methods. Unlike in error minimization, and somewhat surprisingly, performing no pruning led to results that were on par with other methods in terms of the evaluation criteria. The main advantage of pruning was in the reduction of the decision tree size, sometimes by a factor of ten. While no method dominated others on all datasets, even for the same domain different pruning mechanisms are better for different loss matrices. 1 P r u n i n g D e c i s i o n T r e e s Decision trees are a widely used symbolic modeling technique for classification tasks in machine learning. The most common approach to constructing decision tree classifiers is to grow a full tree and prune it back. Pruning is desirable because the tree that is grown may overfit the data by inferring more structure than is justified by the training set. Specifically, if there are no conflicting instances, the training set error of a fully built tree is zero, while the true error is likely to be larger. To combat this overfitting problem, the tree is pruned back with the goal of identifying the tree with the lowest error rate on previously unobserved instances, breaking ties in favor of smaller trees (Breiman, Friedman, Olshen ~c Stone 1984, Quinlan 1993. Several pruning methods have been introduced in the literature, including cost-complexity pruning, reduced error pruning, pessimistic pruning, error-based pruning, penalty pruning, and MDL pruning. Historically, most pruning algorithms have been developed to minimize the expected e r r o r r a t e of the decision tree, assuming that classification errors have the same unit cost.