Decision trees suffer from high variance. A slightly different sample might give you entirely different splits; using decision trees for data interpretation or

Decision trees suffer from high variance. A slightly different sample might give you entirely different splits; using decision trees for data interpretation or feature selection is an art at best (and for some data sets uselessly unreliable). >Decision trees are weak learners. This is untrue. They're only used as weak learners in boosting because the tree depth is limited to some small constant. >Decision trees run fast even with lots of observations and variables I don't know all the decision tree learning algorithms, but at least some of the common ones run in O(features * samples * splits). That's not terrible, but you can handle much larger data sets optimizing w/ stochastic gradient descent or coordinate descent. >Decision trees can easily handle unbalanced datasets. This links to a post about bagging, which is not really specific to decision trees (but can be done with any learning algorithm)