A Gentle Introduction to Decision Tree Learning

Decision tree learning is a method for approximating discrete-valued target functions, which is a part of supervised learning, where we learn a mapping from input to output by analyzing examples for which true values are given by a supervisor or a teacher or a human being which is generally treated as Train-data set and the model trained on train data set is used to classify unseen data, the learned function is represented by a decision tree.


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