It seems to be generally acknowledged that decision trees have low prediction accuracy. Is there a concise explanation for why they have low accuracy?
I've read this so much, I've accepted it to be true, but I realize I don't have any intuition as to why it's true.
As an example, here's an excerpt from Elements of Statistical Learning (page 352):
Trees have one aspect that prevents them from being the ideal tool for predictive learning, namely inaccuracy. They seldom provide predictive ac- curacy comparable to the best that can be achieved with the data at hand.
Or on Wikipedia, under the heading Disadvantages of Decision Trees: "They are often relatively inaccurate. Many other predictors perform better with similar data. "