  There is a large literature explaining why AdaBoost is a successful
classifier. The literature on AdaBoost focuses on classifier margins and
boosting's interpretation as the optimization of an exponential likelihood
function. These existing explanations, however, have been pointed out to be
incomplete. A random forest is another popular ensemble method for which there
is substantially less explanation in the literature. We introduce a novel
perspective on AdaBoost and random forests that proposes that the two
algorithms work for similar reasons. While both classifiers achieve similar
predictive accuracy, random forests cannot be conceived as a direct
optimization procedure. Rather, random forests is a self-averaging,
interpolating algorithm which creates what we denote as a "spikey-smooth"
classifier, and we view AdaBoost in the same light. We conjecture that both
AdaBoost and random forests succeed because of this mechanism. We provide a
number of examples and some theoretical justification to support this
explanation. In the process, we question the conventional wisdom that suggests
that boosting algorithms for classification require regularization or early
stopping and should be limited to low complexity classes of learners, such as
decision stumps. We conclude that boosting should be used like random forests:
with large decision trees and without direct regularization or early stopping.
