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.

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 ...