The following table consists of training data from an employee database. The following table consists of training

The following table consists of training data from an employee database.

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The following table consists of training data from an employee database. The data have been generalized. For example, "31... 35" for age represents the age category with range of 31 to 35. For a given row entry, count represents the number of data tuples having the values for department, status, age, and salary given in that row. department sales sales sales status age senior 31...35 junior 26...30 junior junior senior 31...35 salary 46K...50K count 30 26K... 30K 40 31K...35K 40 20 31...35 21...25 46K... 50K systems systems systems junior 26...30 systems senior 41...45 marketing senior 36...40 marketing junior 31...35 secretary secretary Let status be the class label attribute. 66K... 70K 5 46K...50K 3 66K... 70K 3 46K...50K 10 41K...45K 4 4 senior 46...50 36K... 40K junior 26...30 26K... 30K 6 (a) Construct a decision tree from the given data using information gain. Use R to verify your result and show your code. (b) Given a data tuple having the values "systems", "31...35", and "46K-50K" for the attributes department, age, and salary, respectively, what would a naive Bayesian classification of the status for the tuple be? The following table consists of training data from an employee database. The data have been generalized. For example, "31... 35" for age represents the age category with range of 31 to 35. For a given row entry, count represents the number of data tuples having the values for department, status, age, and salary given in that row. department sales sales sales status age senior 31...35 junior 26...30 junior junior senior 31...35 salary 46K...50K count 30 26K... 30K 40 31K...35K 40 20 31...35 21...25 46K... 50K systems systems systems junior 26...30 systems senior 41...45 marketing senior 36...40 marketing junior 31...35 secretary secretary Let status be the class label attribute. 66K... 70K 5 46K...50K 3 66K... 70K 3 46K...50K 10 41K...45K 4 4 senior 46...50 36K... 40K junior 26...30 26K... 30K 6 (a) Construct a decision tree from the given data using information gain. Use R to verify your result and show your code. (b) Given a data tuple having the values "systems", "31...35", and "46K-50K" for the attributes department, age, and salary, respectively, what would a naive Bayesian classification of the status for the tuple be?