Random Forest
is similar to the decision tree, except the questions that are posed include some randomness. The goal is to push out bias and group outcomes based upon the most likely positive responses. These collections of positive responses are called bags.
An example application of the Random Forest model is the algorithm Netflix uses to recommend movies. It looks at people who have similar tastes and then recommends movies that way. It tosses out outlier answers by using randomness to avoid skewing the response in an incorrect direction.
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