Decision trees are useful for the points enumerated in the blog article. One disadvantage of decision trees is that they can be slow on large data sets (> 1M e

Decision trees are useful for the points enumerated in the blog article. One disadvantage of decision trees is that they can be slow on large data sets (> 1M examples). They are a batch algorithm, which means that you have look at all examples to build a tree, although they can be trained in an mini-online setting (only look at 10K examples per tree) which is faster. More importantly, decision tree induction involves combinatorial optimization over splitting features. This is much slower than a continuous optimization over non-linearities. So decision tree induction is slower than, say, stochastic gradient descent over a neural network. (As hamner points out, there is no "best" data mining algorithm, just like there is no "best" programming language.)