July 2018
Beginner
162 pages
3h 25m
English
Random forests are extensions of decision trees and are a kind of ensemble method.
Ensemble methods can achieve high accuracy by building several classifiers and running a each one independently. When a classifier makes a decision, you can make use of the most common and the average decision. If we use the most common method, it is called voting.
Here's a diagram depicting the ensemble method:

You can think of each classifier as being specialized for a unique perspective on the data. Each classifier may be a different type. For example, you can combine a decision tree and a logistic regression and a neural net, or the classifiers ...
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