Question $2$: The following table consists of training data from an

employee database. The data have been generalized; for example,

$31..\mathrm{.}35"$ for age represents the age range of $31$ to $35.$ The class label

is status.

Department Age Salary Status

Sales $31..\mathrm{.}3546$K$..\mathrm{.}50$K Senior

Sales $26..\mathrm{.}3026$K$..\mathrm{.}30$K Junior

Sales $31..\mathrm{.}3531$K$..\mathrm{.}35$K Junior

Systems $21..\mathrm{.}2546$K$..\mathrm{.}50$K Junior

Systems $31..\mathrm{.}3566$K$..\mathrm{.}70$K Senior

Systems $26..\mathrm{.}3046$K$..\mathrm{.}50$K Junior

Systems $41..\mathrm{.}4566$K$..\mathrm{.}70$K Senior

Marketing $36..\mathrm{.}4046$K$..\mathrm{.}50$K Junior

Marketing $31..\mathrm{.}3541$K$..\mathrm{.}45$K Junior

Marketing $46..\mathrm{.}5036$K$..\mathrm{.}40$K Senior

Sales $26..\mathrm{.}3026$K$..\mathrm{.}30$K Senior

$1.$ Use ID$3$ 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", $"26...30","46$K$..\mathrm{.}50$K$">$

b$.<$ "Sales", $"31...35","26$K$..\mathrm{.}30$K$">$