Consider the dataset shown in Table 1 for a binary classification problem. Table 1: Dataset with 20 instances 1.1 1.1 Moviel D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1.3 Rental Days Format DVD DVD DVD DVD DVD DVD Online Online Comedy Online Comedy Online Documentary DVD Comedy DVD Entertainment Online Entertainment 1 10 3 6 9 3 1 10 9 11 8 6 2 2153 WA W N 3 4 Category Class Days-equal-depth Entertainment Comedy Documentary 3 Comedy Comedy Documentary Comedy Online Documentary Documentary DVD Online Documentary Online Documentary Online Entertainment Online Documentary Online Documentary 0 0 0 0 OOO OOOOHLLL 0 0 0 0 0 0 1 1 Discretize the attribute 'Rental Days' to transform it into a categorical attribute with 4 attribute values, a1, a2, a3, a4 (i.e., number of bins = 4) and fill out the last column. Use the equal-depth approach for discretization. 1 1 1 1 Compute the Entropy, Gini, and Misclassification Error for the overall collection of training examples. (These will be the impurity measures on the parent node.) Compute the combined Entropy, Gini, Misclassification Error of the children nodes for all the three attributes: Rental Days, Format, and Movie Category, using multi-way splits for Rental Days and Movie

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