Question
1
Consider the task of building a
classifier from random data, where the attribute values are generated randomly
irrespective of the class labels. Assume the data set contains records from two
classes, “+” and “−.” Half of the data set is used for training while the
remaining half is used for testing. Suppose there are equal number of “+” and
“--” records in test data set and classifier predicts every test record to be
positive. What is the expected error rate of the classifier on the test data?
Select one:
a.
Information is not enough.
b.
0%
c.
100%
d.
50%
Feedback
The correct answer is: 50%
Question
2
We built a classifier to detect
which gender a name belongs to. We represent each name in term of the following
features.
·
Number of characters in the name.
·
Number of vowels.
·
If name contains character y or not. (1 for yes, and 0 for no)
Training data set is as follows:
Akash: Male
Pinky: Female
State which one of the following is
true?
Select one or more:
a.
The dimensionality of the data under this feature representation is 3.
b.
The dimensionality of the data under this feature representation is 2.
c.
The feature vectors of the training name Pinky is [5,1,1].
d.
The feature vectors of the training name Akash is [5,2,1].
The feature vectors of the training name Akash is [5,2,1].
Feedback
The correct answer is: The
dimensionality of the data under this feature representation is 3., The feature
vectors of the training name Pinky is [5,1,1].
Question
3
Which of the following statements
are true?
Select one or more:
a.
We can use Genetic Algorithm for classification.
b.
Crossover is essential in Genetic Algorithm.
c.
We cannot use Genetic Algorithm for classification.
d.
Mutation is essential in Genetic Algorithm.
Feedback
The correct answer is: Crossover is
essential in Genetic Algorithm., We can use Genetic Algorithm for
classification.
Question
4
Consider the task of building a
classifier from random data, where the attribute values are generated randomly
irrespective of the class labels. Assume the data set contains records from two
classes, “+” and “−.” Half of the data set is used for training while the
remaining half is used for testing. Suppose there are equal number of “+” and
“--” records in test data set and classifier predicts each test record to be
positive class with probability 0.8 and negative class with probability 0.2.
What is the expected error rate of the classifier on the test data?
Select one:
a.
100%
b.
0%
c.
Information is not enough
d.
50%
Feedback
The correct answer is: 50%
Question
5
Which of the following statements
are true about univariate Gaussian Distribution?
Select one or more:
a.
Mean and Mode are different.
b.
Mean and Mode are same.
c.
None of them.
d.
Beta Distribution is prior conjugate.
Feedback
The correct answer is: Mean and Mode
are same., Beta Distribution is prior conjugate.
Question
6
Consider the following
transformation of the feature space where X = (x1,x2)
Φ(x) = (x12, √2x1x2, x22 ). Which one is the valid kernel for
this transformation?
Select one:
a.
K(x,z)=(xTz)
K(x,z)=(xTz)
b.
K(x,z)=( xTz + 1)2
c.
None of them.
d.
K(x,z)=(xTz)2
Feedback
The correct answer is:
K(x,z)=(xTz)2
K(x,z)=(xTz)2
Question
7
Figure illustrates decision
boundaries for two nearest-neighbor classifiers. Which of the following
statements are true?
Select one or more:
a.
Decision boundary 1 belongs to 1 nearest neighbor.
b.
Decision boundary 1 belongs to 3 nearest neighbor.
c.
Decision boundary 2 belongs to 1 nearest neighbor.
d.
Decision boundary 2 belongs to 3 nearest neighbor.
Feedback
The correct answer is: Decision
boundary 1 belongs to 1 nearest neighbor., Decision boundary 2 belongs to 3
nearest neighbor.
Question
8
Which one of the following
statements are true?
Select one or more:
a.
K-NN has slow training time, does not take into account the overall
distribution of the data.
b.
None of them.
c.
Perceptron has slow training time in respect to K-NN.
d.
K-NN has slow testing time, does take into account the overall distribution of
the data.
Feedback
The correct answer is: Perceptron
has slow training time in respect to K-NN.
Question
9
Which one of the following
statements are true?
Select one or more:
a.
Neural network use to capture nonlinear relationship between input and output.
Neural network use to capture nonlinear relationship between input and output.
b.
Perceptron can classify nonlinear separable data accurately.
Perceptron can classify nonlinear separable data accurately.
c.
All of them.
d.
Perceptron can only be used to classify two dimensional input.
Feedback
The correct answer is:
Neural network use to capture nonlinear relationship between input and output.
Neural network use to capture nonlinear relationship between input and output.
Question
10
Which of the options are true for
the given statement.?
Given a set of linearly separable
training examples, we train the perceptron algorithm twice, initializing the
weights differently for each run. The two training procedures traverse the data
points in the same order and are run until convergence.
Select one or more:
a.
two resulting classifiers have the same performance on the test set
b.
two resulting classifiers have the different performance on the test set
c.
two resulting classifiers have the different performance on the training set
d.
two resulting classifiers have the same performance on the training set
Feedback
The correct answer is: two resulting
classifiers have the same performance on the training set, two resulting classifiers
have the different performance on the test set
Question
11
Which of the Boolean function given
below is linearly separable?
Select one or more:
a.
(X XOR Y) AND (X OR Y)
b.
NOT X AND Y
c.
X AND Y AND Z
d.
(X OR Y) AND (X OR Z)
Feedback
The correct answer is: X AND Y AND
Z, NOT X AND Y, (X OR Y) AND (X OR Z)
Question
12
State True/False for the following
statement. The Support Vectors do not change when we train SVM directly on
input and when we map input to the higher dimension and then train SVM.
Select one:
a.
False
b.
True
Feedback
The correct answer is: False
Question
13
We built a classifier to detect
which gender a name belong to. We represent each name in term of the following
features.
·
Number of characters in the name.
·
Number of vowels.
·
If name contains character y or not. (1 for yes, and 0 for no)
Training data set is as follows:
Akash: Male
Pinky: Female
Using the kNN algorithm with k = 1
and Euclidean (L2) distance, we want to predict the gender of name = Whisky.
State which of the following
statements are true?
Select one or more:
a.
L2 distance between Akash and Whisky is 3 and classifier predicts Male.
b.
L2 distance between Akash and Whisky is 2 and classifier predicts Female.
c.
L2 distance between Pinky and Whisky is 1 and classifier predicts Female.
d.
L2 distance between Pinky and Whisky is 2 and classifier predicts Female.
Feedback
The correct answer is: L2 distance
between Pinky and Whisky is 1 and classifier predicts Female.
Question
14
Consider the following training set
in 2-dimentional Euclidean space.
|
Point
|
Coordinate
|
Class
|
|
X1
|
(-1, 1)
|
Negative
|
|
X2
|
(0, 1)
|
Positive
|
|
X3
|
(0, 2)
|
Negative
|
|
X4
|
(1, -1)
|
Positive
|
|
X5
|
(1, 0)
|
Positive
|
|
X6
|
(1, 2)
|
Positive
|
|
X7
|
(2, 2)
|
Negative
|
|
X6
|
(1, 3)
|
Positive
|
Which of the following is true?
Select one or more:
a.
If 8NN classifier is consider, point (0,0) belongs to Positive class.
If 8NN classifier is consider, point (0,0) belongs to Positive class.
b.
If 5NN classifier is consider, point (0,0) belongs to Positive class.
If 5NN classifier is consider, point (0,0) belongs to Positive class.
c.
If 2NN classifier is consider, point (0,0) belongs to Positive class.
d.
If 1NN classifier is consider, point (0,0) belongs to Positive class.
If 1NN classifier is consider, point (0,0) belongs to Positive class.
Feedback
The correct answer is:
If 8NN classifier is consider, point (0,0) belongs to Positive class.,
If 1NN classifier is consider, point (0,0) belongs to Positive class.,
If 5NN classifier is consider, point (0,0) belongs to Positive class., If 2NN classifier is consider, point (0,0) belongs to Positive class.
If 8NN classifier is consider, point (0,0) belongs to Positive class.,
If 1NN classifier is consider, point (0,0) belongs to Positive class.,
If 5NN classifier is consider, point (0,0) belongs to Positive class., If 2NN classifier is consider, point (0,0) belongs to Positive class.
Question
15
Which of the following statement are
true ?
Select one or more:
a.
Genetic algorithm always stuck to the local minimum.
b.
Gradient decent method always stuck to the local minimum.
c.
Genetic algorithm runs faster than Gradient decent method.
d.
All of them.
Feedback
The correct answer is: Gradient
decent method always stuck to the local minimum.
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