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10000CS467122101

PART ‏تا‎

Answer any two full questions, each carries 9 marks.
Explain the Hypothesis space, Version space, Most General hypothesis, Most
specific hypothesis in the context of a classification problem.
Compare Supervised learning and Unsupervised learning with examples.
Describe the backward selection algorithm for implementing the subset
selection procedure for dimensionality reduction.
Illustrate with an example, overfitting and underfitting of data.
Let X be the set of all real numbers. Describe a hypothesis for X for which the
VC dimension is 2. Describe an example for which the VC dimension is 3.
Describe the steps involved in PCA algorithm.

PART C

Answer any two full questions, each carries 9 marks.
A database contains 80 records on a particular topic of which 55 are relevant
to a certain investigation. A search was conducted on that topic and 50 records
were retrieved. Of the 50 records retrieved, 40 were relevant. Construct the
confusion matrix for the search and calculate the precision and recall scores for
the search.
Explain the general MLE method for estimating parameters.
Based on the following data to classify a particular species if its features are

(Slow, Rarely, No). Use naive Bayes algorithm to classify the data.

51. No. Swim Fly Craw! Class
1 Fast No No Fish
2 | 3; २। No Yes Animal
3 Slow No No Animal
4 Fast No No Animal
5 No Short No Bird
6 No Short No Bird
37 No Rarely No Animal
8 Slow No Yes Animal
9 Slow No No Fish
10 Slow No Yes Fish
11 No Long No Bird
12 Fast No No Bird

16 a) Discuss Back propogation algorithm.

b) Discuss the issues in decision tree learning and Identify how to overcome it.

0)

Describe about CART.

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