Decision trees are created by making meaningful decisions as to where to mark boundaries on ranges of attribute values in order to split the instances into 2 or more subcategories, each being represented by different branches of a tree. This process continues, with branches being recursively split into smaller, more specific, branches on different attributes, with the tree leaves being classes. A subsequent walk of the tree with any un-labeled instance would lead to an unambiguous classification.

After a decision tree model is built, it is often pruned. This means that branches which do not add any value to data classification, or branches which, if removed, do not result in a considerable reduction in training data classification accuracy - this accuracy reduction threshold would be pre-specified - are removed, and its sub-trees are combined. The effects of this pruning process can be measured on training data, but effects on unseen test data (or real world data), remain unknown at the time of model training, parameter tuning, and tree pruning.

An unpruned decision tree can lead to overfitting. Overfitting occurs when a data model describes random error or noise, and does not describe the underlying data relationships. Overfitting more accurately fits known data, and in turn is not as good at predicting new data. As a result, this produces too many class outcomes to be useful.

Also, overfitting does not allow for meaningful information to be learned from a model. A tree that is pruned but does not fit the data so well can still be useful as there would be fewer, more meaningful classes. Fewer classes mean that more instances are grouped together, a situation in which there is a better chance that meaningful patterns will emerge and information will be extracted.

Any time that instances are grouped together in fewer classes there is a better chance of patterns being recognized. This is the reason that pruned decision trees, which avoid the overfitting prone to unpruned trees, could be a better choice for learning.

As discussed with supervised vs. unsupervised learning above, you can see that there are obvious trade-offs to pruning a tree vs. deciding against it.