After training decision trees against data, the algorithm is then run against new data in a test set. Before algorithm training, a test set is randomly extracted from the original set.

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However, Decision Trees can suffer from high variance and instability, which can be addressed by Bagging and Random Forests. Bagging involves generating multiple trees on bootstrapped samples of the ...