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The following table consists of training data from an employee database. The data have been generalized; for example, 3 1 . . . 3 5

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 range of 31 to 35. The class label
is status.
Department Age Salary Status
Sales 31...3546K...50K Senior
Sales 26...3026K...30K Junior
Sales 31...3531K...35K Junior
Systems 21...2546K...50K Junior
Systems 31...3566K...70K Senior
Systems 26...3046K...50K Junior
Systems 41...4566K...70K Senior
Marketing 36...4046K...50K Junior
Marketing 31...3541K...45K Junior
Marketing 46...5036K...40K Senior
Sales 26...3026K...30K Senior
1. Use ID3 algorithm, which selects attribute with highest
information gain to construct a decision tree from the given data.
2. Using the Naive Bayes algorithm, how would the following
examples be classified? Make sure to show your work as well as
the final classification.
a. "Marketing", "26...30","46K...50K">
b. "Sales", "31...35","26K...30K">Question 2: The follo wing table consists of training data from an
emplo yee database. The data have been generalized; for example.
"31dots35" for age represents the age range of 31 to 35. The class label
is status.
Use ID3 algorithm, which selects attribute with highest
information gain to construct a decision tree from the given data.
Using the Naive Bayes algorithm, how would the following
e xamples be classified? Make sure to show your work as well as
the final classification.
a. "Marketing", "26..30","46K...50K">
b. "Sales", "31...35","26K..30K"> solve the problem and give me an answer with detailed explanation of each step
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