Machine Learning Internal Examination

In visualization shown above, fit of three different models (in blue line) on same training data. What can you conclude from these visualizations?

1) The training error in first model is higher when compared to second and third model.

2) The best model for this regression problem is the last (third) model, because it has minimum training error.

3) The second model is more robust than first and third because it will perform better on unseen data.

4) The third model is overfitting data as compared to first and second model.

5) All models will perform same because we have not seen the test data.

• 1 and 3

• 1 and 2

• 1,3 and 4

• only 5

The graph represents a regression line predicting Y from X. The values on the graph shows the residuals for each predictions value. Use this information to compute the SSE (sum of the squared errors)

• 3.02

• 0.75

• 1.01

• None of these

In the image, which would be the best value for k assuming that the algorithm you are using is k-Nearest Neighbor.

• 3

• 10

• 20

• 50

Select all true

A) SVM is used for solving Classification problems

B) SVM is used for solving Regression problems

C) SVM is used for solving Clustering problems

D) SMV is a Supervised learning algorithm

E) SVM is a minimum margin classifier

• A

• B

• C

• D

• E

Select all true

A) k-NN algorithm can be used for classification

B) k-NN algorithm can be used for regression

C) k-NN algorithm can be used for both classification & regression

D) k-NN does not require an explicit training step

• A

• B

• C

• D

Which of the following will be Euclidean Distance between the two data point A(1,3) and B(2,3)?

• 1

• 2

• 4

• 8

Suppose, you have given the following data (Fig:A) where x and y are the 2 input variables and Class is the dependent variable.

In Fig:B, a scatter plot which shows the above data in 2D space.

Suppose, you want to predict the class of new data point x=1 and y=1 using Eucludian distance in 3-NN. In which class this data point belong to?

• + Class

• – Class

• Can’t say

• None of these

Which of the following can be used for clustering of data ?

• Single layer perceptron

• Multilayer perceptron

• Self organizing map

• Radial basis function

• All of the above

Back propagation is a learning technique that adjusts weights in the neural network by propagating weight changes.

• Forward from source to sink

• Backward from sink to source

• Forward from source to hidden nodes

• Backward from sink to hidden nodes

• None of the above

In the given graphical model, A, B, C, and D are binary random variables.

How many parameters are needed to define the Conditional Probability Distributions (CPD’s) for this Bayes Net?

• 2

• 8

• 6

• 4