00000CS 467121903

PART C
Answer any two full questions, each carries 9 marks.
14 a) Identify the first splitting attribute for decision tree by using ID3 algorithm (6)

with
the following dataset.
Major Experience Tie
CS programming
CS programming
೮5 management
CS management
business programming
business programming
business management
business management
b) Explain perceptron learning algorithm. (3)

15 2) Suppose 10000 patients get tested for flu; out of them, 9000 are actually (4)
healthy and 1000 are actually sick. For the sick people, a test was positive
for 620 and negative for 380. For the healthy people, the same test was
positive for 180 and negative for 8820. Construct a confusion matrix for the
data and compute the precision and recall for the data.
b) Consider the training data in the following table where Play is a class (5)
attribute. In the table, the Humidity attribute has values “۴۰ (for low) or
“H” (for high), Sunny has values “|? (for yes) or “N” (for no), Wind has
values “57 (for strong) or “W” (for weak), and Play has values “Yes” or

“No”.
Humidity
TS
H
H
H
L

What is class label for the following day (Humidity=L, Sunny=N,
Wind=W), according to naive Bayesian classification?
16 a) What are the benefits of pruning in decision tree induction? Explain different (5)
approaches to tree pruning?
b) Given the set of values X = (3, 9, 11, 5, 2)' and Y = (1, 8, 11, 4,3)". (4)
Evaluate the regression coefficients.
PART 0
Answer any two full questions, each carries 12 marks.
17 a) Explain DBSCAN algorithm for density based clustering. List out its (6)
advantages compared to K-means.

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