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It is a variant of supervised machine learning algorithm founded on the schematic of ensembled learning. Ensemble learning is an algorithm where you join multiple or single algorithm into multiple types of algorithms of multiple or same variant to create a complex and advanced prediction model. It also combines many algorithms of same variant as decision trees, forest trees, etc. so the name “Random Forest”. It is used for regression and classification tasks.

The way it works is it picks a part of the dataset and builds a decision tree on these records, and after selection of number of trees you want this process is repeated. Each tree represents the prediction in that category for which the new record belongs. The only limitation here is that there forte lies in their complexity and for that we need substantial computing resources when huge number of decision trees can be brought together which in turn will better train themselves.