Decision Trees are a type of supervised machine learning in which the data is continually split according to a parameter (you explain what the input is and what the related output is in the training data).
A decision tree has the advantage of forcing the evaluation of all conceivable decision outcomes and tracing each path to a conclusion. It generates a detailed analysis of the effects along each branch and flags decision nodes that require additional investigation.
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