Can you elucidate the principle of a Random Forest in machine learning?

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Anonymous · Accepted Answer

The random forest algorithm is an ensemble method that combines multiple
decision trees to make a prediction. It is very flexible and can be applied to a
large number of tasks. It functions by creating a random decision tree and
fitting it to the data, typically with a subset of features available. The final
prediction is the average (or majority rule) decision.

This is like having a number of interviews evaluating a candidate. Each one will
be interested in a different subset of qualifications, and by averaging them,
all the view points are incorporated.

Can you elucidate the principle of a Random Forest in machine learning?

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