Decision tree encoded a series of if/then/else question to reach a leaf node as output. Cloning a decision tree to a forest of trees can improve the accuracy of the model. Cloning a tree by using a different subset of training data (which allow duplicate use of same data in the sampling). Another variation can use a subset of features. Using these inputs to train a tree, they introduce randomness into the tree formation. Random trees are like wisdom by group.
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