Random Forest is an ensemble learning method that averages the result from an array of decision trees to establish an overall prediction for the outcome.
As with many of the algorithms in this list, Random Forest typically operates as an ‘early’ sorter and filter of data, and as such consistently crops up in new research papers. Some examples of Random Forest usage include Magnetic Resonance Image Synthesis, Bitcoin price prediction, census segmentation, text classification and credit card fraud detection.
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