DETAILED SYLLABUS
UNIT-I
INTRODUCTION – Well defined
learning problems, Designing a Learning System, Issues in Machine Learning; THE
CONCEPT LEARNING TASK - General-to-specific ordering of hypotheses, Find-S,
List then eliminate algorithm, Candidate elimination algorithm, Inductive bias
UNIT-II
DECISION TREE LEARNING - Decision
tree learning algorithm-Inductive bias- Issues in Decision tree learning;
ARTIFICIAL NEURAL NETWORKS –
Perceptrons, Gradient descent and the Delta rule, Adaline, Multilayer networks,
Derivation of backpropagation rule Backpropagation AlgorithmConvergence, Generalization;
UNIT-III
Evaluating Hypotheses: Estimating
Hypotheses Accuracy, Basics of sampling Theory, Comparing Learning Algorithms;
Bayesian Learning: Bayes theorem,
Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian
belief networks, EM algorithm;
UNIT-IV
Computational Learning Theory:
Sample Complexity for Finite Hypothesis spaces, Sample Complexity for Infinite
Hypothesis spaces, The Mistake Bound Model of Learning; INSTANCE-BASED LEARNING
– k-Nearest Neighbour Learning, Locally Weighted Regression, Radial basis
function networks, Case-based learning
UNIT-V
Genetic Algorithms: an
illustrative example, Hypothesis space search, Genetic Programming, Models of
Evolution and Learning; Learning first order rules-sequential covering
algorithms- General to specific beam search-FOIL; REINFORCEMENT LEARNING - The
Learning Task, Q Learning.
1. What
is Machine Learning (ML)?
A. The
autonomous acquisition of knowledge through the use of manual programs
B. The
selective acquisition of knowledge through the use of computer programs
C. The
selective acquisition of knowledge through the use of manual programs
D. The
autonomous acquisition of knowledge through the use of computer programs
Correct option is D
2. Father
of Machine Learning (ML)
A. Geoffrey
Chaucer
B. Geoffrey
Hill
C. Geoffrey
Everest Hinton
D. None
of the above
Correct option is C
3. Which
is FALSE regarding regression?
A. It
may be used for interpretation
B. It
is used for prediction
C. It
discovers causal relationships
D. It
relates inputs to outputs
Correct option is C
4. Choose
the correct option regarding machine learning (ML) and artificial intelligence
(AI)
A. ML
is a set of techniques that turns a dataset into a software
B. AI
is a software that can emulate the human mind
C. ML
is an alternate way of programming intelligent machines
D. All
of the above
Correct option is D
5. Which
of the factors affect the performance of the learner system does not include?
A. Good
data structures
B. Representation
scheme used
C. Training
scenario
D. Type
of feedback
Correct option is A
6. In
general, to have a well-defined learning problem, we must identity which of the
following
A. The
class of tasks
B. The
measure of performance to be improved
C. The
source of experience
D. All
of the above
Correct option is D
7. Successful
applications of ML
A. Learning
to recognize spoken words
B. Learning
to drive an autonomous vehicle
C. Learning
to classify new astronomical structures
D. Learning
to play world-class backgammon
E. All
of the above
Correct option is E
8. Which
of the following does not include different learning methods
A. Analogy
B. Introduction
C. Memorization
D. Deduction
Correct option is B
9. In
language understanding, the levels of knowledge that does not include?
A. Empirical
B. Logical
C. Phonological
D. Syntactic
Correct option is A
10. Designing
a machine learning approach involves:-
A. Choosing
the type of training experience
B. Choosing
the target function to be learned
C. Choosing
a representation for the target function
D. Choosing
a function approximation algorithm
E. All
of the above
Correct option is E
11. Concept
learning inferred a ……………….valued function from training examples of its input
and output.
A. Decimal
B. Hexadecimal
C. Boolean
D. All
of the above
Correct option is C
12. Which
of the following is not a supervised learning?
A. Naive
Bayesian
B. PCA
C. Linear
Regression
D. Decision
Tree
Correct option is B
13. What
is Machine Learning?
i.
Artificial Intelligence
ii.
Deep Learning
iii.
Data Statistics
A. Only
(i)
B. (i)
and (ii)
C. All
D. None
Correct option is B
14. What
kind of learning algorithm for "Facial identities or facial
expressions"?
A. Prediction
B. Recognition
Patterns
C. Generating
Patterns
D. Recognizing
Anomalies
Correct option is B
15. Which
of the following is not type of learning?
A. Unsupervised
Learning
B. Supervised
Learning
C. Semi-unsupervised
Learning
D. Reinforcement
Learning
Correct option is C
16. Real-Time
decisions, Game AI, Learning Tasks, Skill Aquisition, and Robot Navigation are
applications of which of the folowing
A. Supervised
Learning: Classification
B. Reinforcement
Learning
C. Unsupervised
Learning: Clustering
D. Unsupervised
Learning: Regression
Correct option is B
17. Targetted
marketing, Recommended Systems, and Customer Segmentation are applications in
which of the following
A. Supervised
Learning: Classification
B. Unsupervised
Learning: Clustering
C. Unsupervised
Learning: Regression
D. Reinforcement
Learning
Correct option is B
18. Fraud
Detection, Image Classification, Diagnostic, and Customer Retention are
applications in which of the following
A. Unsupervised
Learning: Regression
B. Supervised
Learning: Classification
C. Unsupervised
Learning: Clustering
D. Reinforcement
Learning
Correct option is B
19. Which
of the following is not function of symbolic in the various function
representation of Machine Learning?
A. Rules
in propotional Logic
B. Hidden-Markov
Models (HMM)
C. Rules
in first-order predicate logic
D. Decision
Trees
Correct option is B
20. Which
of the following is not numerical functions in the various function
representation of Machine Learning?
A. Neural
Network
B. Support
Vector Machines
C. Case-based
D. Linear
Regression
Correct option is C
21. FIND-S
Algorithm starts from the most specific hypothesis and generalize it by
considering only examples.
A. Negative
B. Positive
C. Negative
or Positive
D. None
of the above
Correct option is B
22. FIND-S
algorithm ignores ……………….examples.
A. Negative
B. Positive
C. Both
D. None
of the above
Correct option is A
23. The
Candidate-Elimination Algorithm represents the .
A. Solution
Space
B. Version
Space
C. Elimination
Space
D. All
of the above
Correct option is B
24. Inductive
learning is based on the knowledge that if something happens a lot it is likely
to be generally.
A. True
B. False
Correct option is A
25. Inductive
learning takes examples and generalizes rather than starting with ……………….
knowledge.
A. Inductive
B. Existing
C. Deductive
D. None
of these
Correct option is B
26. A
drawback of the FIND-S is that, it assumes the consistency within the training
set.
A. True
B. False
Correct option is A
27. What
strategies can help reduce overfitting in decision trees?
i.
Enforce a maximum depth for the tree
ii.
Enforce a minimum number of samples in leaf
nodes
iii.
Pruning
iv.
Make sure each leaf node is one pure class
A. All
B. (i),
(ii) and (iii)
C. (i),
(iii), (iv)
D. None
Correct option is B
28. Which
of the following is a widely used and effective machine learning algorithm
based on the idea of bagging?
A. Decision
Tree
B. Random
Forest
C. Regression
D. Classification
Correct option is B
29. To
find the minimum or the maximum of a function, we set the gradient to zero
because which of the following
A. Depends
on the type of problem
B. The
value of the gradient at extrema of a function is always zero
C. Both
(A) and (B)
D. None
of these
Correct option is B
A. Decision
trees are prone to be overfit
B. Decision
trees are robust to outliers
C. Factor
analysis
D. None
of the above
Correct option is A
31. What
is perceptron?
A. A
single layer feed-forward neural network with pre-processing
B. A
neural network that contains feedback
C. A
double layer auto-associative neural network
D. An
auto-associative neural network
Correct option is A
32. Which
of the following is true for neural networks?
i.
The training time depends on the size of the
network.
ii.
Neural networks can be simulated on a
conventional computer.
iii.
Artificial neurons are identical in operation to
biological ones.
A. All
B. Only
(ii)
C. (i)
and (ii)
D. None
Correct option is C
33. What
are the advantages of neural networks over conventional computers?
i.
They have the ability to learn by example.
ii.
They are more fault tolerant.
iii.
They are more suited for real time operation due
to their high „computational‟ rates.
A. (i)
and (ii)
B. (i)
and (iii)
C. Only
(i)
D. All
E. None
Correct option is D
34. What
is Neuro software?
A. It
is software used by Neurosurgeon
B. Designed
to aid experts in real world
C. It
is powerful and easy neural network
D. A
software used to analyze neurons
Correct option is C
35. Which
is true for neural networks?
A. Each
node computes it‟s weighted input
B. Node
could be in excited state or non-excited state
C. It
has set of nodes and connections
D. All
of the above
Correct option is D
36. What
is the objective of backpropagation algorithm?
A. To
develop learning algorithm for multilayer feedforward neural network, so that
network can be trained to capture the mapping implicitly
B. To
develop learning algorithm for multilayer feedforward neural network
C. To
develop learning algorithm for single layer feedforward neural network
D. All
of the above
Correct option is A
37. Which
of the following is true?
Single layer associative neural networks do not have the
ability to:-
i.
Perform pattern recognition
ii.
Find the parity of a picture
iii.
Determine whether two or more shapes in a
picture are connected or not
A. (ii)
and (iii)
B. Only
(ii)
C. All
D. None
Correct option is A
38. The
backpropagation law is also known as generalized delta rule.
A. True
B. False
Correct option is A
39. Which
of the following is true?
i.
On average, neural networks have higher
computational rates than conventional computers.
ii.
Neural networks learn by example.
iii.
Neural networks mimic the way the human brain
works.
A. All
B. (ii)
and (iii)
C. (i),
(ii) and (iii)
D. None
Correct option is A
40. What
is true regarding backpropagation rule?
A. Error
in output is propagated backwards only to determine weight updates
B. There
is no feedback of signal at nay stage
C. It
is also called generalized delta rule
D. All
of the above
Correct option is D
41. There
is feedback in final stage of backpropagation algorithm.
A. True
B. False
Correct option is B
42. An
auto-associative network is
A. A
neural network that has only one loop
B. A
neural network that contains feedback
C. A
single layer feed-forward neural network with pre-processing
D. A
neural network that contains no loops
Correct option is B
43. A
3-input neuron has weights 1, 4 and 3. The transfer function is linear with the
constant of proportionality being equal to 3. The inputs are 4, 8 and 5
respectively. What will be the output?
A. 139
B. 153
C. 612
D. 160
Correct option is B
44. What
of the following is true regarding backpropagation rule?
A. Hidden
layers output is not all important, they are only meant for supporting input
and output layers
B. Actual
output is determined by computing the outputs of units for each hidden layer
C. It
is a feedback neural network
D. None
of the above
Correct option is B
45. What
is back propagation?
A. It
is another name given to the curvy function in the perceptron
B. It
is the transmission of error back through the network to allow weights to be
adjusted so that the network can learn
C. It
is another name given to the curvy function in the perceptron
D. None
of the above
Correct option is B
46. The
general limitations of back propagation rule is/are
A. Scaling
B. Slow
convergence
C. Local
minima problem
D. All
of the above
Correct option is D
47. What
is the meaning of generalized in statement “backpropagation is a generalized
delta rule” ?
A. Because
delta is applied to only input and output layers, thus making it more simple
and generalized
B. It
has no significance
C. Because
delta rule can be extended to hidden layer units
D. None
of the above
Correct option is C
48. Neural
Networks are complex …………..functions with many parameters.
A. Linear
B. Non
linear
C. Discreate
D. Exponential
Correct option is A
49. The
general tasks that are performed with backpropagation algorithm
A. Pattern
mapping
B. Prediction
C. Function
approximation
D. All
of the above
Correct option is D
50. Backpropagation
learning is based on the gradient descent along error surface.
A. True
B. False
Correct option is A
51. In
backpropagation rule, how to stop the learning process?
A. No
heuristic criteria exist
B. On
basis of average gradient value
C. There
is convergence involved
D. None
of these
Correct option is B
52. Applications
of NN (Neural Network)
A. Risk
management
B. Data
validation
C. Sales
forecasting
D. All
of the above
Correct option is D
53. The
network that involves backward links from output to the input and hidden layers
is known as
A. Recurrent
neural network
B. Self
organizing maps
C. Perceptrons
D. Single
layered perceptron
Correct option is A
54. Decision
Tree is a display of an algorithm.
A. True
B. False
Correct option is A
55. Which
of the following is/are the decision tree nodes?
A. End
Nodes
B. Decision
Nodes
C. Chance
Nodes
D. All
of the above
Correct option is D
56. End
Nodes are represented by which of the following
A. Solar
street light
B. Triangles
C. Circles
D. Squares
Correct option is B
57. Decision
Nodes are represented by which of the following
A. Solar
street light
B. Triangles
C. Circles
D. Squares
Correct option is D
58. Chance
Nodes are represented by which of the following
A. Solar
street light
B. Triangles
C. Circles
D. Squares
Correct option is C
59. Advantage
of Decision Trees
A. Possible
Scenarios can be added
B. Use
a white box model, if given result is provided by a model
C. Worst,
best and expected values can be determined for different scenarios
D. All
of the above
Correct option is D
60. ………………. terms are required for building a
bayes model.
1
2
3
4
Correct option is C
61. Which
of the following is the consequence between a node and its predecessors while
creating bayesian network?
A. Conditionally
independent
B. Functionally
dependent
C. Both
Conditionally dependant & Dependant
D. Dependent
Correct option is A
62. Why
it is needed to make probabilistic systems feasible in the world?
A. Feasibility
B. Reliability
C. Crucial
robustness
D. None
of the above
Correct option is C
63. Bayes
rule can be used for:-
A. Solving
queries
B. Increasing
complexity
C. Answering
probabilistic query
D. Decreasing
complexity
Correct option is C
64. ………………. provides way and means of weighing up
the desirability of goals and the likelihood of achieving them.
A. Utility
theory
B. Decision
theory
C. Bayesian
networks
D. Probability
theory
Correct option is A
65. Which
of the following provided by the Bayesian Network?
A. Complete
description of the problem
B. Partial
description of the domain
C. Complete
description of the domain
D. All
of the above
Correct option is C
66. Probability
provides a way of summarizing the that
comes from our laziness and ignorances.
A. Belief
B. Uncertaintity
C. Joint
probability distributions
D. Randomness
Correct option is B
67. The
entries in the full joint probability distribution can be calculated as
A. Using
variables
B. Both
Using variables & information
C. Using
information
D. All
of the above
Correct option is C
68. Causal
chain (For example, Smoking cause cancer) gives rise to:-
A. Conditionally
Independence
B. Conditionally
Dependence
C. Both
D. None
of the above
Correct option is A
69. The
bayesian network can be used to any query by using:-
A. Full
distribution
B. Joint
distribution
C. Partial
distribution
D. All
of the above
Correct option is B
70. Bayesian
networks allow compact specification of:-
A. Joint
probability distributions
B. Belief
C. Propositional
logic statements
D. All
of the above
Correct option is A
71. The
compactness of the bayesian network can be described by
A. Fully
structured
B. Locally
structured
C. Partially
structured
D. All
of the above
Correct option is B
72. The
Expectation Maximization Algorithm has been used to identify conserved domains
in unaligned proteins only. State True or False.
A. True
B. False
Correct option is B
73. Which
of the following is correct about the Naive Bayes?
A. Assumes
that all the features in a dataset are independent
B. Assumes
that all the features in a dataset are equally important
C. Both
D. All
of the above
Correct option is C
74. Which
of the following is false regarding EM Algorithm?
A. The
alignment provides an estimate of the base or amino acid composition of each
column in the site
B. The
column-by-column composition of the site already available is used to estimate
the probability of finding the site at any position in each of the sequences
C. The
row-by-column composition of the site already available is used to estimate the
probability
D. None
of the above
Correct option is C
75. Naïve
Bayes Algorithm is a ………………. learning algorithm.
A. Supervised
B. Reinforcement
C. Unsupervised
D. None
of these
Correct option is A
76. EM
algorithm includes two repeated steps, here the step 2 is .
A. The
normalization
B. The
maximization step
C. The
minimization step
D. None
of the above
Correct option is C
77. Examples
of Naïve Bayes Algorithm is/are
A. Spam
filtration
B. Sentimental
analysis
C. Classifying
articles
D. All
of the above
Correct option is D
78. In
the intermediate steps of "EM Algorithm", the number of each base in
each column is determined and then converted to fractions.
A. True
B. False
Correct option is A
79. Naïve
Bayes algorithm is based on ………………. and used for solving classification
problems.
A. Bayes
Theorem
B. Candidate
elimination algorithm
C. EM
algorithm
D. None
of the above
E. Correct
option is A
80. Types
of Naïve Bayes Model:
A. Gaussian
B. Multinomial
C. Bernoulli
D. All
of the above
Correct option is D
81. Disadvantages
of Naïve Bayes Classifier:
A. Naive
Bayes assumes that all features are independent or unrelated, so it cannot
learn the relationship between features.
B. It
performs well in Multi-class predictions as compared to the other Algorithms.
C. Naïve
Bayes is one of the fast and easy ML algorithms to predict a class of datasets.
D. It
is the most popular choice for text classification problems.
Correct option is A
82. The
benefit of Naïve Bayes:-
A. Naïve
Bayes is one of the fast and easy ML algorithms to predict a class of datasets.
B. It
is the most popular choice for text classification problems.
C. It
can be used for Binary as well as Multi-class Classifications.
D. All
of the above
Correct option is D
83. In
which of the following types of sampling the information is carried out under
the opinion of an expert?
A. Convenience
sampling
B. Judgement
sampling
C. Quota
sampling
D. Purposive
sampling
Correct option is B
84. Full
form of MDL.
A. Minimum
Description Length
B. Maximum
Description Length
C. Minimum
Domain Length
D. None
of these
Correct option is A
85. For
the analysis of ML algorithms, we need
A. Computational
learning theory
B. Statistical
learning theory
C. Both
A & B
D. None
of these
Correct option is C
86. PAC
stand for
A. Probably
Approximate Correct
B. Probably
Approx Correct
C. Probably
Approximate Computation
D. Probably
Approx Computation
Correct option is A
87. ………………. of hypothesis h with respect to target
concept c and distribution D , is the probability that h will misclassify an
instance drawn at random according to D
A. True
Error
B. Type
1 Error
C. Type
2 Error
D. None
of these
Correct option is A
88. Statement:
True error defined over entire instance space, not just training data
A. True
B. False
Correct option is A
89. What
are the area CLT comprised of?
A. Sample
Complexity
B. Computational
Complexity
C. Mistake
Bound
D. All
of these
Correct option is D
90. What
area of CLT tells “How many examples we need to find a good hypothesis ?”?
A. Sample
Complexity
B. Computational
Complexity
C. Mistake
Bound
D. None
of these
Correct option is A
91. What
area of CLT tells “How much computational power we need to find a good
hypothesis ?”?
A. Sample
Complexity
B. Computational
Complexity
C. Mistake
Bound
D. None
of these
Correct option is B
92. What
area of CLT tells “How many mistakes we will make before finding a good
hypothesis ?”?
A. Sample
Complexity
B. Computational
Complexity
C. Mistake
Bound
D. None
of these
Correct option is C
93. (For
question no. 9 and 10) Can we say that concept described by conjunctions of
Boolean literals are PAC learnable?
A. Yes
B. No
Correct option is A
94. How
large is the hypothesis space when we have n Boolean attributes?
A. |H|
= 3 n
B. |H|
= 2 n
C. |H|
= 1 n
D. |H|
= 4n
Correct option is A
95. The
VC dimension of hypothesis space H1 is larger than the VC dimension of
hypothesis space H2. Which of the following can be inferred from this?
A. The
number of examples required for learning a hypothesis in H1 is larger than the
number of examples required for H2
B. The
number of examples required for learning a hypothesis in H1 is smaller than the
number of examples required for H2.
C. No
relation to number of samples required for PAC learning.
Correct option is A
96. For
a particular learning task, if the requirement of error parameter changes from
0.1 to
A. How
many more samples will be required for PAC learning?
B. Same
C. 2
times
D. 1000
times
E. 10
times
Correct option is D
97. Computational
complexity of classes of learning problems depends on which of the following?
A. The
size or complexity of the hypothesis space considered by learner
B. The
accuracy to which the target concept must be approximated
C. The
probability that the learner will output a successful hypothesis
D. All
of these
Correct option is D
98. The
instance-based learner is a
A. Lazy-learner
B. Eager
learner
C. Can‟t
say
Correct option is A
99. When
to consider nearest neighbour algorithms?
A. Instance
map to point in kn
B. Not
more than 20 attributes per instance
C. Lots
of training data
D. None
of these
E. A,
B & C
Correct option is E
100.
What are the advantages of Nearest neighbour
alogo?
A. Training
is very fast
B. Can
learn complex target functions
C. Don‟t
lose information
D. All
of these
Correct option is D
101.
What are the difficulties with k-nearest
neighbour algo?
A. Calculate
the distance of the test case from all training cases
B. Curse
of dimensionality
C. Both
A & B
D. None
of these
Correct option is C
102.
What if the target function is real valued in
kNN algo?
A. Calculate
the mean of the k nearest neighbours
B. Calculate
the SD of the k nearest neighbour
C. None
of these
Correct option is A
103.
What is/are true about Distance-weighted KNN?
A. The
weight of the neighbour is considered
B. The
distance of the neighbour is considered
C. Both
A & B
D. None
of these
Correct option is C
104.
What is/are advantage(s) of Distance-weighted
k-NN over k-NN?
A. Robust
to noisy training data
B. Quite
effective when a sufficient large set of training data is provided
C. Both
A & B
D. None
of these
Correct option is C
105.
What is/are advantage(s) of Locally Weighted
Regression?
A. Pointwise
approximation of complex target function
B. Earlier
data has no influence on the new ones
C. Both
A & B
D. None
of these
Correct option is C
106.
The quality of the result depends on (LWR)
A. Choice
of the function
B. Choice
of the kernel function K
C. Choice
of the hypothesis space H
D. All
of these
Correct option is D
107.
How many types of layer in radial basis function
neural networks?
A. 3
B. 2
C. 1
D. 4
Correct option is A, Input layer, Hidden layer, and Output
layer
108.
The neurons in the hidden layer contains
Gaussian transfer function whose output are………………. to the distance from the
centre of the neuron.
A. Directly
B. Inversely
C. equal
D. None
of these
Correct option is B
109.
PNN/GRNN networks have one neuron for each point
in the training file, While RBF network have a variable number of neurons that
is usually
A. less
than the number of training points.
B. greater
than the number of training points
C. equal
to the number of training points
D. None
of these
Correct option is A
110.
Which network is more accurate when the size of
training set between small to medium?
A. PNN/GRNN
B. RBF
C. K-means
clustering
D. None
of these
Correct option is A
111.
What is/are true about RBF network?
A. A
kind of supervised learning
B. Design
of NN as curve fitting problem
C. Use
of multidimensional surface to interpolate the test data
D. All
of these
Correct option is D
112.
Application of CBR
A. Design
B. Planning
C. Diagnosis
D. All
of these
Correct option is A
113.
What is/are advantages of CBR?
A. A
local approx. is found for each test case
B. Knowledge
is in a form understandable to human
C. Fast
to train
D. All
of these
Correct option is D
114.
112 In k-NN algorithm, given a set of training
examples and the value of k < size of training set (n), the algorithm
predicts the class of a test example to be the. What is/are advantages of CBR?
A. Least
frequent class among the classes of k closest training examples.
B. Most
frequent class among the classes of k closest training examples.
C. Class
of the closest point.
D. Most
frequent class among the classes of the k farthest training examples.
Correct option is B
115.
Which of the following statements is true about
PCA?
i.
We must standardize the data before applying
PCA.
ii.
We should select the principal components which
explain the highest variance
iii.
We should select the principal components which
explain the lowest variance
iv.
We can use PCA for visualizing the data in lower
dimensions
A. (i),
(ii) and (iv).
B. (ii)
and (iv)
C. (iii)
and (iv)
D. (i)
and (iii)
Correct option is A
116.
Genetic algorithm is a
A. Search
technique used in computing to find true or approximate solution to
optimization and search problem
B. Sorting
technique used in computing to find true or approximate solution to
optimization and sort problem
C. Both
A & B
None of these
Correct option is A
117.
GA techniques are inspired by ………………biology.
A. Evolutionary
B. Cytology
C. Anatomy
D. Ecology
Correct option is A
118.
When would the genetic algorithm terminate?
A. Maximum
number of generations has been produced
B. Satisfactory
fitness level has been reached for the population.
C. Both
A & B
D. None
of these
Correct option is C
119.
The algorithm operates by iteratively updating a
pool of hypotheses, called the
A. Population
B. Fitness
C. None
of these
Correct option is A
120.
What is the correct representation of GA?
A. GA(Fitness,
Fitness_threshold, p)
B. GA(Fitness,
Fitness_threshold, p, r )
C. GA(Fitness,
Fitness_threshold, p, r, m)
D. GA(Fitness,
Fitness_threshold)
Correct option is C
121.
Genetic operators includes
A. Crossover
B. Mutation
C. Both
A & B
D. None
of these
Correct option is C
122.
Produces two new offspring from two parent string
by copying selected bits from each parent is called
A. Mutation
B. Inheritance
C. Crossover
D. None
of these
Correct option is C
123.
Each schema the set of bit strings containing
the indicated as
A. 0s,
1s
B. only
0s
C. only
1s
D. 0s,
1s, *s
Correct option is D
124.
0*10 represents the set of bit strings that
includes exactly
A. 0010,
0110
B. 0010,
0010
C. 0100,
0110
D. 0100,
0010
Correct option is A
125.
Correct ( h ) is the percent of all training
examples correctly classified by hypothesis h. then Fitness function is equal
to
A. Fitness
( h) = (correct ( h)) 2
B. Fitness
( h) = (correct ( h)) 3
C. Fitness
( h) = (correct ( h))
D. Fitness
( h) = (correct ( h)) 4
Correct option is A
126.
Statement: Genetic Programming individuals in
the evolving population are computer programs rather than bit strings.
A. True
B. False
Correct option is A
127.
………………. evolution
over many generations was directly influenced by the experiences of individual
organisms during their lifetime
A. Baldwin
B. Lamarckian
C. Bayes
D. None
of these
Correct option is B
128.
Search through the hypothesis space cannot be
characterized. Why?
A. Hypotheses
are created by crossover and mutation operators that allow radical changes
between successive generations
B. Hypotheses
are not created by crossover and mutation operators.
C. None
of these
Correct option is A
129.
ILP stand for
A. Inductive
Logical programming
B. Inductive
Logic Programming
C. Inductive
Logical Program
D. Inductive
Logic Program
Correct option is B
130.
What is/are the requirement for the
Learn-One-Rule method?
A. Input,
accepts a set of +ve and -ve training examples.
B. Output,
delivers a single rule that covers many +ve examples and few -ve.
C. Output
rule has a high accuracy but not necessarily a high coverage.
D. A
& B
E. A,
B & C
Correct option is E
131.
………………. is
any predicate (or its negation) applied to any set of terms.
A. Literal
B. Null
C. Clause
D. None
of these
Correct option is A
132.
Ground literal is a literal that
A. Contains
only variables
B. does
not contains any functions
C. does
not contains any variables
D. Contains
only functions
Correct option is C
133.
………………. emphasizes
learning feedback that evaluates the learner's performance without providing
standards of correctness in the form of behavioural targets.
A. Reinforcement
learning
B. Supervised
Learning
C. None
of these
Correct option is A
134.
Features of Reinforcement learning
A. Set
of problem rather than set of techniques
B. RL
is training by reward and punishments.
C. RL
is learning from trial and error with the world.
D. All
of these
Correct option is D
135.
Which type of feedback used by RL?
A. Purely
Instructive feedback
B. Purely
Evaluative feedback
C. Both
A & B
D. None
of these
Correct option is B
136.
What is/are the problem solving methods for RL?
A. Dynamic
programming
B. Monte
Carlo Methods
C. Temporal-difference
learning
D. All
of these
Correct option is D
137.
The FIND-S Algorithm
A. Starts
with starts from the most specific hypothesis
B. It
considers negative examples only.
C. It
considers both negative and positive examples.
D. None
of these
Correct option is A
138.
The hypothesis space has a general-to-specific
ordering of hypotheses, and the search can be efficiently organized by taking
advantage of a naturally occurring structure over the hypothesis space
A. TRUE
B. FALSE
Correct option is A
139.
The Version space is:
A. The
subset of all hypotheses is called the version space with respect to the
hypothesis space H and the training examples D, because it contains all plausible
versions of the target concept.
B. The
version space consists of only specific hypotheses.
C. None
of these
Correct option is A
140.
The Candidate-Elimination Algorithm
A. The
key idea in the Candidate-Elimination algorithm is to output a description of
the set of all hypotheses consistent with the training examples.
B. Candidate-Elimination
algorithm computes the description of this set without explicitly enumerating
all of its members.
C. This
is accomplished by using the more-general-than partial ordering and maintaining
a compact representation of the set of consistent hypotheses.
D. All
of these
Correct option is D
141.
Concept learning is basically acquiring the
definition of a general category from given sample positive and negative
training examples of the category.
A. TRUE
B. FALSE
Correct option is A
142.
The hypothesis h1 is more-general-than
hypothesis h2 ( h1 > h2) if and only if h1≥h2 is true and h2≥h1 is false. We
also say h2 is more-specific-than h1
A. The
statement is true
B. The
statement is false
C. We
cannot conclude.
D. None
of these
Correct option is A
143.
The List-Then-Eliminate Algorithm
A. The
List-Then-Eliminate algorithm initializes the version space to contain all
hypotheses in H, then eliminates any hypothesis found inconsistent with any
training example.
B. The
List-Then-Eliminate algorithm not initializes to the version space.
C. None
of these
Correct option is A
144.
What will take place as the agent observes its
interactions with the world?
A. Learning
B. Hearing
C. Perceiving
D. Speech
Correct option is A
145.
Which modifies the performance element so that
it makes better decision? Performance element
A. Performance
element
B. Changing
element
C. Learning
element
D. None
of the mentioned
Correct option is C
146.
Any hypothesis found to approximate the target
function well over a sufficiently large set of training examples will also
approximate the target function well over other unobserved example is called:
A. Inductive
Learning Hypothesis
B. Null
Hypothesis
C. Actual
Hypothesis
D. None
of these
Correct option is A
147.
Feature of ANN in which ANN creates its own
organization or representation of information it receives during learning time
is
A. Adaptive
Learning
B. Self
Organization
C. What-If
Analysis
D. Supervised
Learning
Correct option is B
148.
How the decision tree reaches its decision?
A. Single
test
B. Two
test
C. Sequence
of test
D. No
test
Correct option is C
149.
Which of the following is a disadvantage of
decision trees?
A. Factor
analysis
B. Decision
trees are robust to outliers
C. Decision
trees are prone to be overfit
D. None
of the above
Correct option is C
150.
Tree/Rule based classification algorithms
generate which rule to perform the classification.
A. if-then.
B. while.
C. do
while.
D. switch.
Correct option is A
151.
What is Gini Index?
A. It
is a type of index structure
B. It
is a measure of purity
C. None
of the options
Correct option is A
152.
What is not a RNN in machine learning?
A. One
output to many inputs
B. Many
inputs to a single output
C. RNNs
for nonsequential input
D. Many
inputs to many outputs
Correct option is A
153.
Which of the following sentences are correct in
reference to Information gain?
A. It
is biased towards multi-valued attributes
B. ID3
makes use of information gain
C. The
approach used by ID3 is greedy
D. All
of these
Correct option is D
154.
A Neural Network can
A. For
Loop questions
B. what-if
questions
C. IF-The-Else
Analysis Questions
D. None
of these
Correct option is B
155.
Artificial neural network used for
A. Pattern
Recognition
B. Classification
C. Clustering
D. All
Correct option is D
156.
Which of the following are the advantage/s of
Decision Trees?
A. Possible
Scenarios can be added
B. Use
a white box model, If given result is provided by a model
C. Worst,
best and expected values can be determined for different scenarios
D. All
of the mentioned
Correct option is D
157.
What is the mathematical likelihood that
something will occur?
A. Classification
B. Probability
C. Naïve
Bayes Classifier
D. None
of the others are correct.
Correct option is C
158.
What does the Bayesian network provides?
A. Complete
description of the domain
B. Partial
description of the domain
C. Complete
description of the problem
D. None
of the mentioned
Correct option is C
159.
Where does the Bayes rule can be used?
A. Solving
queries
B. Increasing
complexity
C. Decreasing
complexity
D. Answering
probabilistic query
Correct option is D
160.
How many terms are required for building a Bayes
model?
A. 2
B. 3
C. 4
D. 1
Correct option is B
161.
What is needed to make probabilistic systems
feasible in the world?
A. Reliability
B. Crucial
robustness
C. Feasibility
D. None
of the mentioned
Correct option is B
162.
It was shown that the Naive Bayesian method
A. Can
be much more accurate than the optimal Bayesian method
B. Is
always worse off than the optimal Bayesian method
C. Can
be almost optimal only when attributes are independent
D. Can
be almost optimal when some attributes are dependent
Correct option is C
163.
What is the consequence between a node and its
predecessors while creating Bayesian network?
A. Functionally
dependent
B. Dependant
C. Conditionally
independent
D. Both
Conditionally dependant & Dependant
Correct option is C
164.
How the compactness of the Bayesian network can
be described?
A. Locally
structured
B. Fully
structured
C. Partial
structure
D. All
of the mentioned
Correct option is A
165.
How the entries in the full joint probability
distribution can be calculated?
A. Using
variables
B. Using
information
C. Both
Using variables & information
D. None
of the mentioned
Correct option is B
166.
How the Bayesian network can be used to any
query?
A. Full
distribution
B. Joint
distribution
C. Partial
distribution
D. All
of the mentioned
Correct option is B
167.
Sample Complexity is
A. The
sample complexity is the number of training-samples that we need to supply to
the algorithm, so that the function returned by the algorithm is within an
arbitrarily small error of the best possible function, with probability
arbitrarily close to 1
B. How
many training examples are needed for learner to converge to a successful
hypothesis.
C. All
of these
Correct option is C
168.
PAC stands for
A. Probability
Approximately Correct
B. Probability
Applied Correctly
C. Partition
Approximately Correct
Correct option is A
169.
Which of the following will be true about k in
k-NN in terms of variance
A. When
you increase the k the variance will increases
B. When
you decrease the k the variance will increases
C. Can‟t
say
D. None
of these
Correct option is B
170.
Which of the following option is true about k-NN
algorithm?
A. It
can be used for classification
B. It
can be used for regression
C. It
can be used in both classification and regression
Correct option is C
171.
In k-NN it is very likely to overfit due to the
curse of dimensionality. Which of the following option would you consider to
handle such problem?
1.
Dimensionality Reduction, 2. Feature selection
A. 1
B. 2
C. 1
and 2
D. None
of these
Correct option is C
172.
When you find noise in data which of the
following option would you consider in k- NN
A. I
will increase the value of k
B. I
will decrease the value of k
C. Noise
can not be dependent on value of k
D. None
of these
Correct option is A
173.
Which of the following will be true about k in
k-NN in terms of Bias?
A. When
you increase the k the bias will be increases
B. When
you decrease the k the bias will be increases
C. Can‟t
say
D. None
of these
Correct option is A
174.
What is used to mitigate overfitting in a test
set?
A. Overfitting
set
B. Training
set
C. Validation
dataset
D. Evaluation
set
Correct option is C
175.
A radial basis function is a
A. Activation
function
B. Weight
C. Learning
rate
D. none
Correct option is A
176.
Mistake Bound is
A. How
many training examples are needed for learner to converge to a successful
hypothesis.
B. How
much computational effort is needed for a learner to converge to a successful
hypothesis
C. How
many training examples will the learner misclassify before conversing to a
successful hypothesis
D. None
of these
Correct option is C
177.
All of the following are suitable problems for
genetic algorithms EXCEPT
A. dynamic
process control
B. pattern
recognition with complex patterns
C. simulation
of biological models
D. simple
optimization with few variables
Correct option is D
178.
Adding more basis functions in a linear model...
(Pick the most probably option)
A. Decreases
model bias
B. Decreases
estimation bias
C. Decreases
variance
D. Doesn‟t
affect bias and variance
Correct option is A
179.
Which of these are types of crossover
A. Single
point
B. Two
point
C. Uniform
D. All
of these
Correct option is D
180.
A feature F1 can take certain value: A, B, C, D,
E, & F and represents grade of students from a college. Which of the
following statement is true in following case?
A. Feature
F1 is an example of nominal variable.
B. Feature
F1 is an example of ordinal variable.
C. It
doesn‟t belong to any of the above category.
Correct option is B
181.
You observe the following while fitting a linear
regression to the data: As you increase the amount of training data, the test
error decreases and the training error increases. The train error is quite low
(almost what you expect it to), while the test error is much higher than the train
error. What do you think is the main reason behind this behaviour? Choose the
most probable option.
A. High
variance
B. High
model bias
C. High
estimation bias
D. None
of the above
Correct option is C
182.
Genetic algorithms are heuristic methods that do
not guarantee an optimal solution to a problem
A. TRUE
B. FALSE
Correct option is A
183.
Which of the following statements about
regularization is not correct?
A. Using
too large a value of lambda can cause your hypothesis to underfit the data.
B. Using
too large a value of lambda can cause your hypothesis to overfit the data.
C. Using
a very large value of lambda cannot hurt the performance of your hypothesis.
D. None
of the above
Correct option is A
184.
Consider the following: (a) Evolution (b)
Selection (c) Reproduction (d) Mutation Which of the following are found in
genetic algorithms?
A. All
B. a,
b, c
C. a,
b
D. b,
d
Correct option is A
185.
Genetic Algorithm are a part of
A. Evolutionary
Computing
B. inspired
by Darwin's theory about evolution - "survival of the fittest"
C. are
adaptive heuristic search algorithm based on the evolutionary ideas of natural
selection and genetics
D. All
of the above
Correct option is D
186.
Genetic algorithms belong to the family of
methods in the
A. artificial
intelligence area
B. optimization
area.
C. complete
enumeration family of methods
D. Non-computer
based (human) solutions area
Correct option is A
187.
For a two player chess game, the environment
encompasses the opponent
A. True
B. False
Correct option is A
188.
Which among the following is not a necessary
feature of a reinforcement learning solution to a learning problem?
A. exploration
versus exploitation dilemma
B. trial
and error approach to learning
C. learning
based on rewards
D. representation
of the problem as a Markov Decision Process
Correct option is D
189.
Which of the following sentence is FALSE
regarding reinforcement learning
A. It
relates inputs to outputs.
B. It
is used for prediction.
C. It
may be used for interpretation.
D. It
discovers causal relationships.
Correct option is D
190.
The EM algorithm is guaranteed to never decrease
the value of its objective function on any iteration
A. TRUE
B. FALSE
Correct option is A
191.
Consider the following modification to the
tic-tac-toe game: at the end of game, a coin is tossed and the agent wins if a
head appears regardless of whatever has happened in the game.Can reinforcement
learning be used to learn an optimal policy of playing Tic-Tac-Toe in this
case?
A. Yes
B. No
Correct option is B
192.
Out of the two repeated steps in EM algorithm,
the step 2 is _
A. the
maximization step
B. the
minimization step
C. the
optimization step
D. the
normalization step
Correct option is A
193.
Suppose the reinforcement learning player was
greedy, that is, it always played the move that brought it to the position that
it rated the best. Might it learn to play better, or worse, than a non greedy
player?
A. Worse
B. Better
Correct option is B
194.
A chess agent trained by using Reinforcement
Learning can be trained by playing against a copy of the same agent.
A. True
B. False
Correct option is A
195.
The EM iteration alternates between performing
an expectation (E) step, which creates a function for the expectation of the
log-likelihood evaluated using the current estimate for the parameters, and a
maximization (M) step, which computes parameters maximizing the expected
log-likelihood found on the E step.
A. TRUE
B. FALSE
Correct option is A
196.
Expectation–maximization (EM) algorithm is an
A. Iterative
B. Incremental
C. None
Correct option is A
197.
Feature need to be identified by using Well
Posed Learning Problem:
A. Class
of tasks
B. Performance
measure
C. Training
experience
D. All
of these
Correct option is D
198.
A computer program that learns to play checkers
might improve its performance as:
A. Measured
by its ability to win at the class of tasks involving playing checkers
B. Experience
obtained by playing games against itself.
C. Both
a & b
D. None
of these
Correct option is C
199.
Learning symbolic representations of concepts
known as:
A. Artificial
Intelligence
B. Machine
Learning
C. Both
a & b
D. None
of these
Correct option is A
200.
The field of study that gives computers the
capability to learn without being explicitly programmed
A. Machine
Learning
B. Artificial
Intelligence
C. Deep
Learning
D. Both
a & b
Correct option is A
201.
The autonomous acquisition of knowledge through
the use of computer programs is called
A. Artificial
Intelligence
B. Machine
Learning
C. Deep
learning
D. All
of these
Correct option is B
202.
Learning that enables massive quantities of data
is known as
A. Artificial
Intelligence
B. Machine
Learning
C. Deep
learning
D. All
of these
Correct option is B
203.
A different learning method does not include
A. Memorization
B. Analogy
C. Deduction
D. Introduction
Correct option is D
204.
Types of learning used in machine learning.
A. Supervised
B. Unsupervised
C. Reinforcement
D. All
of these
Correct option is D
205.
A computer program is said to learn from
experience E with respect to some class of tasks T and performance measure P,
if its performance at tasks in T, as measured by P, improves with experience
A. Supervised
learning problem
B. Un
Supervised learning problem
C. Well
posed learning problem
D. All
of these
Correct option is C
206.
Which of the following is a widely used and
effective machine learning algorithm based on the idea of bagging?
A. Decision
Tree
B. Regression
C. Classification
D. Random
Forest
Correct option is D
207.
How many types are available in machine
learning?
A. 1
B. 2
C. 3
D. 4
Correct option is C
208.
A model can learn based on the rewards it
received for its previous action is known as:
A. Supervised
learning
B. Unsupervised
learning
C. Reinforcement
learning
D. Concept
learning
Correct option is C
209.
A subset of machine learning that involves
systems that think and learn like humans using artificial neural networks.
A. Artificial
Intelligence
B. Machine
Learning
C. Deep
Learning
D. All
of these
Correct option is C
210.
A learning method in which a training data
contains a small amount of labeled data and a large amount of unlabeled data is
known as
A. Supervised
Learning
B. Semi
Supervised Learning
C. Unsupervised
Learning
D. Reinforcement
Learning
Correct option is C
211.
Methods used for the calibration in Supervised
Learning
A. Platt
Calibration
B. Isotonic
Regression
C. All
of these
D. None
of above
Correct option is C
212.
The basic design issues for designing a learning
systems.
A. Choosing
the Training Experience
B. Choosing
the Target Function
C. Choosing
a Function Approximation Algorithm
D. Estimating
Training Values
E. All
of these
Correct option is E
213.
In Machine learning the module that must solve
the given performance task is known as:
A. Critic
B. Generalizer
C. Performance
system
D. All
of these
Correct option is C
214.
A learning method that is used to solve a
particular computational program, multiple models such as classifiers or
experts are strategically generated and combined is called as
A. Supervised
Learning
B. Semi
Supervised Learning
C. Unsupervised
Learning
D. Reinforcement
Learning
E. Ensemble
learning
Correct option is E
215.
In a learning system the component that takes as
takes input the current hypothesis (currently learned function) and outputs a
new problem for the Performance System to explore.
A. Critic
B. Generalizer
C. Performance
system
D. Experiment
generator
E. All
of these
Correct option is D
216.
Learning method that is used to improve the
classification, prediction, function approximation etc of a model.
A. Supervised
Learning
B. Semi
Supervised Learning
C. Unsupervised
Learning
D. Reinforcement
Learning
E. Ensemble
learning
Correct option is E
217.
In a learning system the component that takes as
input the history or trace of the game and produces as output a set of training
examples of the target function is known as:
A. Critic
B. Generalizer
C. Performance
system
D. All
of these
Correct option is A
218.
The most common issue when using ML is
A. Lack
of skilled resources
B. Inadequate
Infrastructure
C. Poor
Data Quality
D. None
of these
Correct option is C
219.
How to ensure that your model is not over
fitting
A. Cross
validation
B. Regularization
C. All
of these
D. None
of these
Correct option is C
220.
A way to ensemble multiple classifications or
regression model.
A. Stacking
B. Bagging
C. Blending
D. Boosting
Correct option is A
221.
How well a model is going to generalize in new
environment is known as
A. Data
Quality
B. Transparent
C. Implementation
D. None
of these
Correct option is B
222.
Common classes of problems in machine learning
is
A. Classification
B. Clustering
C. Regression
D. All
of these
Correct option is D
223.
Which of the following is a widely used and
effective machine learning algorithm based on the idea of bagging?
A. Decision
Tree
B. Regression
C. Classification
D. Random
Forest
Correct option is D
224.
Cost complexity pruning algorithm is used in?
A. CART
B. C4.5
C. ID3
D. All
of these.
E. Answer
Correct option is A
225.
Which one of these is not a tree based learner?
A. CART
B. C4.5
C. ID3
D. Bayesian
Classifier
Correct option is D
226.
Which one of these is a tree based learner?
A. Rule
based
B. Bayesian
Belief Network
C. Bayesian
classifier
D. Random
Forest
Correct option is D
227.
What is the approach of basic algorithm for
decision tree induction?
A. Greedy
B. Top
Down
C. Procedural
D. Step
by Step
Correct option is A
228.
Which of the following classifications would
best suit the student performance classification systems?
A. If-.then-analysis
B. Market-basket
analysis
C. Regression
analysis
D. Cluster
analysis
Correct option is A
229.
What are two steps of tree pruning work?
A. Pessimistic
pruning and Optimistic pruning
B. Post
pruning and Pre pruning
C. Cost
complexity pruning and time complexity pruning
D. None
of these.
Correct option is B
230.
How will you counter over-fitting in decision
tree?
A. By
pruning the longer rules
B. By
creating new rules
C. Both
By pruning the longer rules‟ and „ By creating new rules‟
D. None
of these.
Correct option is A
231.
Which of the following sentences are true?
A. In
pre-pruning a tree is 'pruned' by halting its construction early
B. A
pruning set of class labeled tuples is used to estimate cost complexity.
C. The
best pruned tree is the one that minimizes the number of encoding
D. bits.
E. All
of the above.
Correct option is D
232.
Which of the following is a disadvantage of
decision trees?
A. Factor
analysis
B. Decision
trees are robust to outliers
C. Decision
trees are prone to be over fit
D. None
of the above
Correct option is C
233.
In which of the following scenario a gain ratio
is preferred over Information Gain?
A. When
a categorical variable has very large number of category
B. When
a categorical variable has very small number of category
C. Number
of categories is the not the reason
D. None
of these
Correct option is A
234.
Major pruning techniques used in decision tree
are
A. Minimum
error
B. Smallest
tree
C. Both
a & b
D. None
of these
Correct option is B
235.
What does the central limit theorem state?
A. If
the sample size increases sampling distribution must approach normal
distribution
B. If
the sample size decreases then the sample distribution must approach normal
distribution.
C. If
the sample size increases then the sampling distributions much approach an
exponential distribution.
D. If
the sample size decreases then the sampling distributions much approach an
exponential distribution.
Correct option is A
236.
The difference between the sample value expected
and the estimates value of the parameter is called as?
A. Bias
B. Error
C. Contradiction
D. Difference
Correct option is A
237.
In which of the following types of sampling the
information is carried out under the opinion of an expert?
A. Quota
sampling
B. Convenience
sampling
C. Purposive
sampling
D. Judgment
sampling
Correct option is D
238.
Which of the following is a subset of
population?
A. Distribution
B. Sample
C. Data
D. Set
Correct option is B
239.
The sampling error is defined as?
A. Difference
between population and parameter
B. Difference
between sample and parameter
C. Difference
between population and sample
D. Difference
between parameter and sample
Correct option is C
240.
Machine learning is interested in the best
hypothesis h from some space H, given observed training data D. Here best
hypothesis means
A. Most
general hypothesis
B. Most
probable hypothesis
C. Most
specific hypothesis
D. None
of these
Correct option is B
241.
Practical difficulties with Bayesian Learning :
A. Initial
knowledge of many probabilities is required
B. No
consistent hypothesis
C. Hypotheses
make probabilistic predictions
D. None
of these
Correct option is A
242.
Bayes' theorem states that the relationship
between the probability of the hypothesis before getting the evidence P(H) and
the probability of the hypothesis after getting the evidence P(H∣E)
is
A. [P(E∣H)P(H)]
/ P(E)
B. [P(E∣H)
P(E) ] / P(H)
C. [P(E)
P(H) ] / P(E∣H)
D. None
of these
Correct option is A
243.
A doctor knows that Cold causes fever 50% of the
time. Prior probability of any patient having cold is 1/50,000. Prior
probability of any patient having fever is 1/20 If a patient has fever, what is
the probability he/she has cold?
A. P(C/F)=
0.0003
B. P(C/F)=0.0004
C. P(C/F)=
0.0002
D. P(C/F)=0.0045
Correct option is C
244.
Which of the following will be true about k in
K-Nearest Neighbor in terms of Bias?
A. When
you increase the k the bias will be increases
B. When
you decrease the k the bias will be increases
C. Can‟t
say
D. None
of these
Correct option is A
245.
When you find noise in data which of the
following option would you consider in K- Nearest Neighbor?
A. I
will increase the value of k
B. I
will decrease the value of k
C. Noise
cannot be dependent on value of k
D. None
of these
Correct option is A
246.
In K-Nearest Neighbor it is very likely to
overfit due to the curse of dimensionality. Which of the following option would
you consider to handle such problem?
1. Dimensionality
Reduction
2. Feature
selection
A. 1
B. 2
C. 1
and 2
D. None
of these
E. Correct
option is C
247.
Radial basis functions is closely related to
distance-weighted regression, but it is
A. lazy
learning
B. eager
learning
C. concept
learning
D. none
of these
Correct option is B
248.
Radial basis function networks provide a global
approximation to the target function, represented by ………………. of many local
kernel functions.
A. a
series combination
B. a
linear combination
C. a
parallel combination
D. a
non linear combination
Correct option is B
249.
The most significant phase in a genetic
algorithm is
A. Crossover
B. Mutation
C. Selection
D. Fitness
function
Correct option is A
250.
The crossover operator produces two new
offspring from
A. Two
parent strings, by copying selected bits from each parent
B. One
parent strings, by copying selected bits from selected parent
C. Two
parent strings, by copying selected bits from one parent
D. None
of these
Correct option is A
251.
Mathematically characterize the evolution over
time of the population within a GA based on the concept of
A. Schema
B. Crossover
C. Don‟t
care
D. Fitness
function
Correct option is A
252.
In genetic algorithm process of selecting
parents which mate and recombine to create off-springs for the next generation
is known as:
A. Tournament
selection
B. Rank
selection
C. Fitness
sharing
D. Parent
selection
Correct option is D
253.
Crossover operations are performed in genetic
programming by replacing
A. Randomly
chosen sub tree of one parent program by a sub tree from the other parent
program.
B. Randomly
chosen root node tree of one parent program by a sub tree from the other parent
program
C. Randomly
chosen root node tree of one parent program by a root node tree from the other
parent program
D. None
of these
Correct option is A
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