Random Forests Algorithm
One of the most popular methods or frameworks used by data scientists at the Rose Data Science Professional Practice Group is Random Forests. The Random Forests algorithm is one of the best among classification algorithms – able to classify large amounts of data with accuracy.
Random Forests are an ensemble learning method (also thought of as a form of nearest neighbor predictor) for classification and regression that construct a number of decision trees at training time and outputting the class that is the mode of the classes output by individual trees (Random Forests is a trademark of Leo Breiman and Adele Cutler for an ensemble of decision trees).
Random Forests are a combination of tree predictors where each tree depends on the values of a random vector sampled independently with the same distribution for all trees in the forest. The basic principle is that a group of “weak learners” can come together to form a “strong learner”. Random Forests are a wonderful tool for making predictions considering they do not overfit because of the law of large numbers. Introducing the right kind of randomness makes them accurate classifiers and regressors.