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The following table consists of training data from an employee database. The data have been gener - alized. For example, 3 1 dots 3
The following table consists of training data from an employee database. The data have been gener
alized. For example, dots for age represents the age range of to 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.
Let status be the class label attribute.
a How would you modify the basic decision tree algorithm to take into consideration the count of
each generalized data tuple ie of each row entry
b Use your algorithm to construct a decision tree from the given data.
c Given a data tuple having the values "systems", dots and for the attributes
department, age, and salary, respectively, what would a nave Bayesian classification of the status
for the tuple be
please give me step by step clear calculation including entropy with count
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