The document discusses random forest, an ensemble machine learning technique that builds decision trees on various sub-samples of a dataset and averages the results. It provides examples of random forest, describes how it works by constructing multiple decision trees and outputting the class that is the mode of the classes of the individual trees, and covers features such as its ability to handle large datasets, give estimates of what variables are important, and prevent overfitting.

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Examples of Random Forest

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Random Forest: An example

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Random Forest: An example

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Random Forest: An example

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Random Forest: An example

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What is Random Forest?

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How Random Forest works?

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Features of Random Forest

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Features of Random Forest

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Applications

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Possible algorithms

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