MACHINE LEARNING

Which of the following is true about Naive Bayes?
(i) Assumes that all the features in a dataset are equally important
(ii) Assumes that all the features in a dataset are independent
(ii) Both (i) and (ii)
(iv) None of the above

What is a support vector?
(i) The distance between any two data points
(ii) The average distance between all data points
(iii) The distance between any two boundary data points .
(iv)The minimum distance between any two data points

The feature of ANN in which ANN creates its own organization or representation of information it receives during learning time is
(i) adaptive learning
(ii) self-organization
(iii) what-if analysis
(iv) supervised learning

In neural network. which of the following techniques is used to deal with over fitting? ×
(i) Dropout
(ii) Regularization.
(iii) Batch normalization
(iv) All of the above

Which of the following is true about averaging ensemble?
(i) It can only be used in classification problem
(ii) It can only be used in regression problem
(ii) It can be used in both classification as well as regression
(iv) None of the above

Which of the following is the best algorithm for text classification?
(i) KNN -
(ii) Decision tree
(iii) Random forest
(iv) Naive Bayes

What is decision tree algorithm? What are the issues in decision tree induction?

How does Back Propagation Rule help in learning in Neural Network?

Explain how the linear maximum-margin classifier can be modified to be non-linear and to allow misclassification of the training examples. Give a derivation of the modified optimization problem needed for training. You need only derive a statement of the primal optimization problem.

Discuss various issues in Decision Tree Learning.

Discuss with examples of instance-based learning algorithms.

Following is a data set that contains two attributes, X and Y, and two class labels, ‘+’ and ‘–’.
Each attribute can take three different values: 0, 1, or 2.

The concept for the ‘+’ class is Y = 1
and the concept for the ‘–’ class is X = 0 ∨ X = 2.

(a)
Build a decision tree on the data set.
Does the tree capture the ‘+’ and ‘–’ concepts?

(b)
What are the accuracy, precision, recall and F1-measure of the decision tree?
(Note that precision, recall, and F1-measure are defined with respect to the ‘+’ class.)

State the mathematical formulation of the SVM problem. Give an outline of the method for solving the problem.

For the Sun Burn data set given below, construct a decision tree.

Name	Hair	Height	Weight	Location	Class
Sunita	Blonde	Average	Light	No	Yes
Anita	Blonde	Tall	Average	Yes	No
Kavita	Brown	Short	Average	Yes	No
Sushma	Blonde	Short	Average	No	Yes
Xavier	Red	Average	Heavy	No	Yes
Balaji	Brown	Tall	Heavy	No	No
Ramesh	Brown	Average	Heavy	No	No
Sweta	Blonde	Short	Light	Yes	No

Explain the bagging and boosting with example.

Discuss the steps in K-means algorithm and evaluate the following table using K-means :

Discuss the Algorithmic convergence & Generalization property of ANN.

What are the applications of supervised machine learning in modern business?

What is ANN? Explain feed forward and back propagation algorithm with example.

Write short notes on any two of the following:

(a) Naive Bayes
(b) Logistic Regression
(c) Back Propagation algorithm