Random forest classification is a type of machine learning algorithm that is used for classification tasks. Classification is a type of supervised learning in which the goal is to predict a discrete class label (such as “spam” or “not spam”) for a given input. Random forest classification is an ensemble method, which means that it trains multiple decision trees on random subsets of the data and then averages their predictions to make a final prediction. This can provide a more robust and accurate model than a single decision tree, especially for complex and non-linear data. In random forest classification, the final prediction is made by taking the majority vote of the predicted class labels from all of the individual decision trees in the ensemble.
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