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What is Understanding Random Forest Classifier about?

Random forest classifiers are an ensemble learning method that constructs a collection of decision trees and uses a voting system to improve classification accuracy. It works by splitting a training set into subsets, constructing decision trees on each subset using randomly selected features each time, then having the trees vote on the correct class for new samples. This combines the ideas of random selection of features and bagging to create a model with low variance and reduced error compared to a single

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gavrila_1

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