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I am new to the field of AI and am reading about decision trees. I am referring to the AIMA book which is pretty much the standard Intro to AI book recommended. In the chapter on decision trees, they discuss in the book a case wherein after the first attribute splits and there are no attributes left but both positive and negative examples have still not been separated, it means that these examples have exactly the same description.... The solution to this case that they suggest is "to return the plurality classification of the remaining examples". I was wondering what that part in bold means? What does it mean to return the 'plurality classification' of a set of examples?
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Plurality is just the generalization of majority to more than 2 classes. It just means take the most frequent class in that leaf and return that as your prediction.
Introduction Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter.
Decision trees can be divided into two types; categorical variable and continuous variable decision trees.
Decision Trees can be used to solve both classification and regression problems. The algorithm can be thought of as a graphical tree-like structure that uses various tuned parameters to predict the results. The decision trees apply a top-down approach to the dataset that is fed during training.
They would have said the majority class if there were only two classes. Plurality is just the generalization of majority to more than 2 classes. It just means take the most frequent class in that leaf and return that as your prediction. For example, if you are classifying the colors of balls, and there are 3 blue balls, 2 red balls, and 2 white balls in a leaf, return blue as your prediction.
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