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#+title: random-forests
#+date: <2018-05-15>
[[https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/info][Stanford
Lagunita's statistical learning course]] has some excellent lectures on
random forests. It starts with explanations of decision trees, followed
by bagged trees and random forests, and ends with boosting. From these
lectures it seems that:
1. The term "predictors" in statistical learning = "features" in machine
learning.
2. The main idea of random forests of dropping predictors for individual
trees and aggregate by majority or average is the same as the idea of
dropout in neural networks, where a proportion of neurons in the
hidden layers are dropped temporarily during different minibatches of
training, effectively averaging over an emsemble of subnetworks. Both
tricks are used as regularisations, i.e. to reduce the variance. The
only difference is: in random forests, all but a square root number
of the total number of features are dropped, whereas the dropout
ratio in neural networks is usually a half.
By the way, here's a comparison between statistical learning and machine
learning from the slides of the Statistcal Learning course:
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