Random forest is an algorithm used to create a collection of decision trees $($also known as a $$forest$)$ from the training data. By using random selection and different subsets of the training data, each tree in the forest produces a slightly different model. The result is a diverse set of predictions which can be averaged out to achieve accurate results. This method tends to be more accurate than traditional single$-$tree decision tree models due to its ability to capture complex relationships between different features in the data.

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