Data analysts at Universal bank is building a classification tree to classify its customers into two classes: nonacceptors (class 0) and acceptors (class 1) of personal loan offer. Each customer can be described by a set of attributes, such as age, experience, income, family size, education, average spending on credit cards per month, etc. The data analysts are in the process of identifying the most powerful predictor to split a set of training records (denoted by A). After splitting A with customer's family size, two subsets of records are generated, denoted by A1 and A2 respectively. The number of nonacceptors and acceptors in A, A1, and A2 are given below. A A1 A2 Number of Nonacceptors 352 340 12 Number of Acceptors 223 36 187 Round your answers to 3 digits after the decimal point. The Gini index of A is 0.475 The Gini index of A1 is 0.173 The Gini index of A2 is 0.113 The combined Gini index of A1 and A2 is 0.648 The reduction in Gini index if training records are split using customer's family size is -1.4147

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