MCQ Question of Machine learning
MCQ Question of Machine learning
 What is Machine
Learning (ML)?
 The autonomous
acquisition of knowledge through the use of manual programs
 The selective
acquisition of knowledge through the use of computer programs
 The selective
acquisition of knowledge through the use of manual programs
 The autonomous
acquisition of knowledge through the use of computer programs
Correct option is D
 Father of Machine
Learning (ML)
 Geoffrey Chaucer
 Geoffrey Hill
 Geoffrey Everest
Hinton
 None of the
above
Correct option is C
 Which is FALSE
regarding regression?
 It may be used for
interpretation
 It is used for
prediction
 It discovers causal
relationships
 It relates inputs to
outputs
Correct option is C
 Choose the correct
option regarding machine learning (ML) and artificial intelligence (AI)
 ML is a set of
techniques that turns a dataset into a software
 AI is a software that
can emulate the human mind
 ML is an alternate
way of programming intelligent machines
 All of the
above
Correct option is D
 Which of the factors
affect the performance of the learner system does not include?
 Good data structures
 Representation scheme
used
 Training scenario
 Type of feedback
Correct option is A
 In general, to have a
welldefined learning problem, we must identity which of the following
 The class of tasks
 The measure of
performance to be improved
 The source of
experience
 All of the
above
Correct option is D
 Successful
applications of ML
 Learning to recognize
spoken words
 Learning to drive an
autonomous vehicle
 Learning to classify
new astronomical structures
 Learning to play
worldclass backgammon
 All of the
above
Correct option is E
 Which of the
following does not include different learning methods
 Analogy
 Introduction
 Memorization
 Deduction
Correct option is B
 In language
understanding, the levels of knowledge that does not include?
 Empirical
 Logical
 Phonological
 Syntactic
Correct option is A
 Designing a machine
learning approach involves:
 Choosing the type of
training experience
 Choosing the target
function to be learned
 Choosing a
representation for the target function
 Choosing a function
approximation algorithm
 All of the
above
Correct option is E
 Concept learning
inferred a
valued function from training examples of its input and
output.
 Decimal
 Hexadecimal
 Boolean
 All of the
above
Correct option is C
 Which of the
following is not a supervised learning?
 Naive Bayesian
 PCA
 Linear Regression
 Decision Tree Answer
Correct option is B
 What is Machine
Learning?
 Artificial
Intelligence
 Deep Learning
 Data Statistics
A.
Only (i)
B.
(i) and (ii)
C.
All
D.
None
Correct option is B
 What kind of learning
algorithm for “Facial identities or facial expressions”?
 Prediction
 Recognition Patterns
 Generating Patterns
 Recognizing Anomalies
Answer
Correct option is B
 Which of the
following is not type of learning?
 Unsupervised Learning
 Supervised Learning
 Semiunsupervised
Learning
 Reinforcement
Learning
Correct option is C
 RealTime decisions,
Game AI, Learning Tasks, Skill Aquisition, and Robot Navigation are applications
of which of the folowing
 Supervised Learning:
Classification
 Reinforcement
Learning
 Unsupervised
Learning: Clustering
 Unsupervised
Learning: Regression
Correct option is B
 Targetted marketing,
Recommended Systems, and Customer Segmentation are applications in which
of the following
 Supervised Learning:
Classification
 Unsupervised
Learning: Clustering
 Unsupervised
Learning: Regression
 Reinforcement
Learning
Correct option is B
 Fraud Detection,
Image Classification, Diagnostic, and Customer Retention are applications
in which of the following
 Unsupervised
Learning: Regression
 Supervised Learning:
Classification
 Unsupervised
Learning: Clustering
 Reinforcement
Learning
Correct option is B
 Which of the
following is not function of symbolic in the various function
representation of Machine Learning?
 Rules in propotional
Logic
 HiddenMarkov Models
(HMM)
 Rules in firstorder
predicate logic
 Decision Trees
Correct option is B
 Which of the
following is not numerical functions in the various function
representation of Machine Learning?
 Neural Network
 Support Vector
Machines
 Casebased
 Linear
Regression
Correct option is C
 FINDS Algorithm
starts from the most specific hypothesis and generalize it by considering
only
 Negative
 Positive
 Negative or Positive
 None of the
above
Correct option is B
 FINDS algorithm
ignores
 Negative
 Positive
 Both
 None of the
above
Correct option is A
 The
CandidateElimination Algorithm represents the .
 Solution Space
 Version Space
 Elimination Space
 All of the above
Correct option is B
 Inductive learning is
based on the knowledge that if something happens a lot it is likely to be
generally
 True
 False Answer
Correct option is A
 Inductive learning
takes examples and generalizes rather than starting with
 Inductive
 Existing
 Deductive
 None of these
Correct option is B
 A drawback of the
FINDS is that it assumes the consistency within the training set
 True
 False
Correct option is A
 What strategies can
help reduce overfitting in decision trees?
 Enforce a maximum
depth for the tree
 Enforce a minimum
number of samples in leaf nodes
 Pruning
 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
 Which of the
following is a widely used and effective machine learning algorithm based
on the idea of bagging?
 Decision Tree
 Random Forest
 Regression
 Classification
Correct option is B
 To find the minimum
or the maximum of a function, we set the gradient to zero because which of
the following
 Depends on the type
of problem
 The value of the
gradient at extrema of a function is always zero
 Both (A) and (B)
 None of these
Correct option is B
 Which of the
following is a disadvantage of decision trees?
 Decision trees are
prone to be overfit
 Decision trees are
robust to outliers
 Factor analysis
 None of the above
Correct option is A
 What is perceptron?
 A single layer
feedforward neural network with preprocessing
 A neural network that
contains feedback
 A double layer
autoassociative neural network
 An autoassociative
neural network
Correct option is A
 Which of the
following is true for neural networks?
 The training time
depends on the size of the
 Neural networks can
be simulated on a conventional
 Artificial neurons
are identical in operation to biological
A.
All
B.
Only (ii)
C.
(i) and (ii)
D.
None
Correct option is C
 What are the
advantages of neural networks over conventional computers?
 They have the ability
to learn by
 They are more fault
 They are more suited
for real time operation due to their high „computational‟
A.
(i) and (ii)
B.
(i) and (iii)
C.
Only (i)
D.
All
E.
None
Correct option is D
 What is Neuro
software?
 It is software used
by Neurosurgeon
 Designed to aid
experts in real world
 It is powerful and
easy neural network
 A software used to
analyze neurons
Correct option is C
 Which is true for
neural networks?
 Each node computes it‟s weighted input
 Node could be in
excited state or nonexcited state
 It has set of nodes
and connections
 All of the above
Correct option is D
 What is the objective
of backpropagation algorithm?
 To develop learning
algorithm for multilayer feedforward neural network, so that network can
be trained to capture the mapping implicitly
 To develop learning
algorithm for multilayer feedforward neural network
 To develop learning
algorithm for single layer feedforward neural network
 All of the above
Correct option is A
 Which of the
following is true?
Single layer associative neural networks do not have the
ability to:
 Perform pattern
recognition
 Find the parity of a
picture
 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
 The backpropagation
law is also known as generalized delta rule
 True
 False
Correct option is A
 Which of the
following is true?
 On average, neural
networks have higher computational rates than conventional computers.
 Neural networks learn
by
 Neural networks mimic
the way the human brain
A.
All
B.
(ii) and (iii)
C.
(i), (ii) and (iii)
D.
None
Correct option is A
 What is true
regarding backpropagation rule?
 Error in output is
propagated backwards only to determine weight updates
 There is no feedback
of signal at nay stage
 It is also called
generalized delta rule
 All of the
above
Correct option is D
 There is feedback in
final stage of backpropagation
 True
 False
Correct option is B
 An autoassociative
network is
 A neural network that
has only one loop
 A neural network that
contains feedback
 A single layer
feedforward neural network with preprocessing
 A neural network that
contains no loops
Correct option is B
 A 3input 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?
 139
 153
 162
 160
Correct option is B
 What of the following
is true regarding backpropagation rule?
 Hidden layers output
is not all important, they are only meant for supporting input and output
layers
 Actual output is
determined by computing the outputs of units for each hidden layer
 It is a feedback
neural network
 None of the
above
Correct option is B
 What is back
propagation?
 It is another name
given to the curvy function in the perceptron
 It is the
transmission of error back through the network to allow weights to be
adjusted so that the network can learn
 It is another name
given to the curvy function in the perceptron
 None of the above
Correct option is B
 The general
limitations of back propagation rule is/are
 Scaling
 Slow convergence
 Local minima problem
 All of the
above
Correct option is D
 What is the meaning
of generalized in statement “backpropagation is a generalized delta rule”
?
 Because delta is
applied to only input and output layers, thus making it more simple and
generalized
 It has no
significance
 Because delta rule
can be extended to hidden layer units
 None of the
above
Correct option is C
 Neural Networks are
complex functions with many
parameter
 Linear
 Non linear
 Discreate
 Exponential
Correct option is A
 The general tasks
that are performed with backpropagation algorithm
 Pattern mapping
 Prediction
 Function
approximation
 All of the
above
Correct option is D
 Backpropagaion
learning is based on the gradient descent along error surface.
 True
 False
Correct option is A
 In backpropagation
rule, how to stop the learning process?
 No heuristic criteria
exist
 On basis of average
gradient value
 There is convergence
involved
 None of these
Correct option is B
 Applications of NN
(Neural Network)
 Risk management
 Data validation
 Sales forecasting
 All of the
above
Correct option is D
 The network that
involves backward links from output to the input and hidden layers is
known as
 Recurrent neural
network
 Self organizing maps
 Perceptrons
 Single layered
perceptron
Correct option is A
 Decision Tree is a
display of an Algorithm?
 True
 False
Correct option is A
 Which of the
following is/are the decision tree nodes?
 End Nodes
 Decision Nodes
 Chance Nodes
 All of the
above
Correct option is D
 End Nodes are
represented by which of the following
 Solar street light
 Triangles
 Circles
 Squares
Correct option is B
 Decision Nodes are
represented by which of the following
 Solar street light
 Triangles
 Circles
 Squares
Correct option is D
 Chance Nodes are
represented by which of the following
 Solar street light
 Triangles
 Circles
 Squares
Correct option is C
 Advantage of Decision
Trees
 Possible Scenarios
can be added
 Use a white box
model, if given result is provided by a model
 Worst, best and
expected values can be determined for different scenarios
 All of the above
Correct option is D
 terms are required
for building a bayes model.
 1
 2
 3
 4
Correct option is C
 Which of the
following is the consequence between a node and its predecessors while
creating bayesian network?
 Conditionally
independent
 Functionally
dependent
 Both Conditionally
dependant & Dependant
 Dependent
Correct option is A
 Why it is needed to
make probabilistic systems feasible in the world?
 Feasibility
 Reliability
 Crucial robustness
 None of the
above
Correct option is C
 Bayes rule can be
used for:
 Solving queries
 Increasing complexity
 Answering
probabilistic query
 Decreasing
complexity
Correct option is C
 provides way and
means of weighing up the desirability of goals and the likelihood of
achieving
 Utility theory
 Decision theory
 Bayesian networks
 Probability
theory
Correct option is A
 Which of the
following provided by the Bayesian Network?
 Complete description
of the problem
 Partial description
of the domain
 Complete description
of the domain
 All of the
above
Correct option is C
65. Probability provides a way of
summarizing the that comes
from our laziness and
 Belief
 Uncertaintity
 Joint probability
distributions
 Randomness
Correct option is B
 The entries in the
full joint probability distribution can be calculated as
 Using variables
 Both Using variables
& information
 Using information
 All of the
above
Correct option is C
 Causal chain (For
example, Smoking cause cancer) gives rise to:
 Conditionally
Independence
 Conditionally
Dependence
 Both
 None of the
above
Correct option is A
 The bayesian network
can be used to answer any query by using:
 Full distribution
 Joint distribution
 Partial distribution
 All of the above
Correct option is B
 Bayesian networks
allow compact specification of:
 Joint probability
distributions
 Belief
 Propositional logic
statements
 All of the
above
Correct option is A
 The compactness of
the bayesian network can be described by
 Fully structured
 Locally structured
 Partially structured
 All of the
above
Correct option is B
 The
ExpectationMaximization Algorithm has been used to identify conserved
domains in unaligned proteins only. State True or False.
 True
 False
Correct option is B
 Which of the
following is correct about the Naive Bayes?
 Assumes that all the
features in a dataset are independent
 Assumes that all the
features in a dataset are equally important
 Both
 All of the
above
Correct option is C
 Which of the
following is false regarding EM Algorithm?
 The alignment
provides an estimate of the base or amino acid composition of each column
in the site
 The columnbycolumn
composition of the site already available is used to estimate the
probability of finding the site at any position in each of the sequences
 The rowbycolumn
composition of the site already available is used to estimate the
probability
 None of the
above
Correct option is C
 NaÃ¯ve Bayes Algorithm
is a learning algorithm.
 Supervised
 Reinforcement
 Unsupervised
 None of these
Correct option is A
 EM algorithm includes
two repeated steps, here the step 2 is
.
 The normalization
 The maximization step
 The minimization step
 None of the
above
Correct option is C
 Examples of NaÃ¯ve
Bayes Algorithm is/are
 Spam filtration
 Sentimental analysis
 Classifying articles
 All of the
above
Correct option is D
 In the intermediate
steps of “EM Algorithm”, the number of each base in each column is
determined and then converted to
 True
 False
Correct option is A
 NaÃ¯ve Bayes algorithm
is based on and used for solving classification problems.
 Bayes Theorem
 Candidate elimination
algorithm
 EM algorithm
 None of the
above
Correct option is A
 Types of NaÃ¯ve Bayes
Model:
 Gaussian
 Multinomial
 Bernoulli
 All of the
above
Correct option is D
 Disadvantages of
NaÃ¯ve Bayes Classifier:
 Naive Bayes assumes
that all features are independent or unrelated, so it cannot learn the
relationship between
 It performs well in
Multiclass predictions as compared to the other
 NaÃ¯ve Bayes is one of
the fast and easy ML algorithms to predict a class of
 It is the most
popular choice for text classification problems.
Correct option is A
 The benefit of NaÃ¯ve
Bayes:
 NaÃ¯ve Bayes is one of
the fast and easy ML algorithms to predict a class of
 It is the most
popular choice for text classification problems.
 It can be used for
Binary as well as Multiclass
 All of the above
Correct option is D
 In which of the
following types of sampling the information is carried out under the
opinion of an expert?
 Convenience sampling
 Judgement sampling
 Quota sampling
 Purposive sampling
Correct option is B
 Full form of MDL?
 Minimum Description
Length
 Maximum Description
Length
 Minimum Domain Length
 None of these
Correct option is A
 For the analysis of
ML algorithms, we need
 Computational
learning theory
 Statistical learning
theory
 Both A & B
 None of these
Correct option is C
 PAC stand for
 Probably Approximate
Correct
 Probably Approx
Correct
 Probably Approximate
Computation
 Probably Approx
Computation
Correct option is A
86. 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.
 True Error
 Type 1 Error
 Type 2 Error
 None of these
Correct option is A
 Statement: True error
defined over entire instance space, not just training data
 True
 False
Correct option is A
 What are the area CLT
comprised of?
 Sample Complexity
 Computational
Complexity
 Mistake Bound
 All of these
Correct option is D
 What area of CLT
tells “How many examples we need to find a good hypothesis ?”?
 Sample Complexity
 Computational
Complexity
 Mistake Bound
 None of these
Correct option is A
 What area of CLT
tells “How much computational power we need to find a good hypothesis ?”?
 Sample Complexity
 Computational
Complexity
 Mistake Bound
 None of these
Correct option is B
 What area of CLT
tells “How many mistakes we will make before finding a good hypothesis ?”?
 Sample Complexity
 Computational
Complexity
 Mistake Bound
 None of these
Correct option is C
 (For question no. 9
and 10) Can we say that concept described by conjunctions of Boolean
literals are PAC learnable?
 Yes
 No
Correct option is A
 How large is the
hypothesis space when we have n Boolean attributes?
 H = 3 ^{n}
 H = 2 ^{n}
 H = 1 ^{n}
 H = 4^{n}
Correct option is A
 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?
 The number of
examples required for learning a hypothesis in H1 is larger than the
number of examples required for H2
 The number of
examples required for learning a hypothesis in H1 is smaller than the
number of examples required for
 No relation to number
of samples required for PAC learning.
Correct option is A
 For a particular
learning task, if the requirement of error parameter changes from 0.1 to
0.01. How many more samples will be required for PAC learning?
 Same
 2 times
 1000 times
 10 times
Correct option is D
 Computational
complexity of classes of learning problems depends on which of the
following?
 The size or
complexity of the hypothesis space considered by learner
 The accuracy to which
the target concept must be approximated
 The probability that
the learner will output a successful hypothesis
 All of these
Correct option is D
 The instancebased
learner is a
 Lazylearner
 Eager learner
 Can‟t say
Correct option is A
 When to consider
nearest neighbour algorithms?
 Instance map to point
in k^{n}
 Not more than 20
attributes per instance
 Lots of training data
 None of these
 A, B & C
Correct option is E
 What are the
advantages of Nearest neighbour alogo?
 Training is very fast
 Can learn complex
target functions
 Don‟t lose information
 All of these
Correct option is D
 What are the
difficulties with knearest neighbour algo?
 Calculate the
distance of the test case from all training cases
 Curse of
dimensionality
 Both A & B
 None of these
Correct option is C
 What if the target
function is real valued in kNN algo?
 Calculate the mean of
the k nearest neighbours
 Calculate the SD of
the k nearest neighbour
 None of these
Correct option is A
 What is/are true
about Distanceweighted KNN?
 The weight of the
neighbour is considered
 The distance of the
neighbour is considered
 Both A & B
 None of these
Correct option is C
 What is/are
advantage(s) of Distanceweighted kNN over kNN?
 Robust to noisy
training data
 Quite effective when
a sufficient large set of training data is provided
 Both A & B
 None of these
Correct option is C
 What is/are
advantage(s) of Locally Weighted Regression?
 Pointwise
approximation of complex target function
 Earlier data has no
influence on the new ones
 Both A & B
 None of these
Correct option is C
 The quality of the
result depends on (LWR)
 Choice of the
function
 Choice of the kernel
function K
 Choice of the
hypothesis space H
 All of these
Correct option is D
 How many types of
layer in radial basis function neural networks?
 3
 2
 1
 4
Correct option is A, Input layer, Hidden layer, and
Output layer
 The neurons in the
hidden layer contains Gaussian transfer function whose output are
to the distance from the
centre of the neuron.
 Directly
 Inversely
 equal
 None of these
Correct option is B
 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
 less than the number
of training
 greater than the
number of training points
 equal to the number
of training points
 None of these
Correct option is A
 Which network is more
accurate when the size of training set between small to medium?
 PNN/GRNN
 RBF
 Kmeans clustering
 None of these
Correct option is A
 What is/are true
about RBF network?
 A kind of supervised
learning
 Design of NN as curve
fitting problem
 Use of
multidimensional surface to interpolate the test data
 All of these
Correct option is D
 Application of CBR
 Design
 Planning
 Diagnosis
 All of these
Correct option is A
 What is/are advantages
of CBR?
 A local approx. is
found for each test case
 Knowledge is in a
form understandable to human
 Fast to train
 All of these
Correct option is D
112 In kNN 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?
 Least frequent class
among the classes of k closest training
 Most frequent class
among the classes of k closest training
 Class of the closest
 Most frequent class
among the classes of the k farthest training examples.
Correct option is B
 Which of the
following statements is true about PCA?
 We must standardize
the data before applying
 We should select the
principal components which explain the highest variance
 We should select the
principal components which explain the lowest variance
 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
 Genetic algorithm is
a
 Search technique used
in computing to find true or approximate solution to optimization and
search problem
 Sorting technique
used in computing to find true or approximate solution to optimization and
sort problem
 Both A & B
 None of these
Correct option is A
 GA techniques are
inspired by
 Evolutionary
 Cytology
 Anatomy
 Ecology
Correct option is A
 When would the
genetic algorithm terminate?
 Maximum number of
generations has been produced
 Satisfactory fitness
level has been reached for the
 Both A & B
 None of these
Correct option is C
 The algorithm
operates by iteratively updating a pool of hypotheses, called the
 Population
 Fitness
 None of these
Correct option is A
 What is the correct
representation of GA?
 GA(Fitness,
Fitness_threshold, p)
 GA(Fitness,
Fitness_threshold, p, r )
 GA(Fitness,
Fitness_threshold, p, r, m)
 GA(Fitness,
Fitness_threshold)
Correct option is C
 Genetic operators
includes
 Crossover
 Mutation
 Both A & B
 None of these
Correct option is C
 Produces two new
offspring from two parent string by copying selected bits from each parent
is called
 Mutation
 Inheritance
 Crossover
 None of these
Correct option is C
 Each schema the set
of bit strings containing the indicated as
 0s, 1s
 only 0s
 only 1s
 0s, 1s, *s
Correct option is D
 0*10 represents the
set of bit strings that includes exactly (A) 0010, 0110
 0010, 0010
 0100, 0110
 0100, 0010
Correct option is A
 Correct ( h ) is the
percent of all training examples correctly classified by hypothesis then
Fitness function is equal to
 Fitness ( h) =
(correct ( h)) ^{2}
 Fitness ( h) =
(correct ( h)) ^{3}
 Fitness ( h) =
(correct ( h))
 Fitness ( h) =
(correct ( h)) ^{4}
Correct option is A
 Statement: Genetic
Programming individuals in the evolving population are computer programs
rather than bit
 True
 False
Correct option is A
 evolution over many
generations was directly influenced by the experiences of individual
organisms during their lifetime
 Baldwin
 Lamarckian
 Bayes
 None of these
Correct option is B
 Search through the
hypothesis space cannot be characterized. Why?
 Hypotheses are
created by crossover and mutation operators that allow radical changes
between successive generations
 Hypotheses are not
created by crossover and mutation
 None of these
Correct option is A
 ILP stand for
 Inductive Logical
programming
 Inductive Logic
Programming
 Inductive Logical
Program
 Inductive Logic
Program
Correct option is B
 What is/are the
requirement for the LearnOneRule method?
 Input, accepts a set
of +ve and ve training examples.
 Output, delivers a
single rule that covers many +ve examples and few ve.
 Output rule has a
high accuracy but not necessarily a high
 A & B
 A, B & C
Correct option is E
 is any predicate (or
its negation) applied to any set of terms.
 Literal
 Null
 Clause
 None of these
Correct option is A
 Ground literal is a
literal that
 Contains only
variables
 does not contains any
functions
 does not contains any
variables
 Contains only
functions Answer
Correct option is C
 emphasizes learning
feedback that evaluates the learner’s performance without providing
standards of correctness in the form of behavioural
 Reinforcement
learning
 Supervised Learning
 None of these
Correct option is A
 Features of
Reinforcement learning
 Set of problem rather
than set of techniques
 RL is training by
reward and
 RL is learning from
trial and error with the
 All of these
Correct option is D
 Which type of
feedback used by RL?
 Purely Instructive
feedback
 Purely Evaluative
feedback
 Both A & B
 None of these
Correct option is B
 What is/are the
problem solving methods for RL?
 Dynamic programming
 Monte Carlo Methods
 Temporaldifference
learning
 All of these
Correct option is D
 The FINDS Algorithm
A.
Starts with starts from the most specific hypothesis
Answer
B.
It considers negative examples
C.
It considers both negative and positive
D.
None of these Correct
136. The hypothesis space has a generaltospecific
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
137. The Version space is:
 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
 The version space
consists of only specific
 None of these

Correct option is A
 The
CandidateElimination Algorithm
A.
The key idea in the CandidateElimination algorithm is to
output a description of the set of all hypotheses consistent with the training
B.
CandidateElimination algorithm computes the description
of this set without explicitly enumerating all of its
C.
This is accomplished by using the moregeneralthan
partial ordering and maintaining a compact representation of the set of
consistent
D.
All of these
Correct option is D
 Concept learning is
basically acquiring the definition of a general category from given sample
positive and negative training examples of the
A.
TRUE
B.
FALSE
Correct option is A
 The hypothesis h1 is
moregeneralthan hypothesis h2 ( h1 > h2) if and only if h1≥h2 is true
and h2≥h1 is false. We also say h2 is morespecificthan h1
A.
The statement is true
B.
The statement is false
C.
We cannot
D.
None of these
Correct option is A
 The
ListThenEliminate Algorithm
A.
The ListThenEliminate algorithm initializes the version
space to contain all hypotheses in H, then eliminates any hypothesis found
inconsistent with any training
B.
The ListThenEliminate algorithm not initializes to the
version
C.
None of these Answer
Correct option is A
 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
 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
 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
 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.
WhatIf Analysis
D.
Supervised Learning
Correct option is B
 How the decision tree
reaches its decision?
A.
Single test
B.
Two test
C.
Sequence of test
D.
No test
Correct option is C
 Which of the
following is a disadvantage of decision trees?
·
Factor analysis
·
Decision trees are robust to outliers
·
Decision trees are prone to be overfit
·
None of the above
Correct option is C
 Tree/Rule based
classification algorithms generate which rule to perform the
classification.
A.
ifthen.
B.
then
C.
do
D.
Answer
Correct option is A
 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
 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
 Which of the
following sentences are correct in reference to Information gain?
A.
It is biased towards multivalued attributes
B.
ID3 makes use of information gain
C.
The approach used by ID3 is greedy
D.
All of these
Correct option is D
 A Neural Network can
answer
A.
For Loop questions
B.
whatif questions
C.
IFTheElse Analysis Questions
D.
None of these Answer
Correct option is B
 Artificial neural
network used for
A.
Pattern Recognition
B.
Classification
C.
Clustering
D.
All Answer
Correct option is D
 Which of the
following are the advantage/s of Decision Trees?
 Possible Scenarios
can be added
 Use a white box
model, If given result is provided by a model
 Worst, best and
expected values can be determined for different scenarios
 All of the mentioned
Correct option is D
 What is the
mathematical likelihood that something will occur?
A.
Classification
B.
Probability
C.
NaÃ¯ve Bayes Classifier
D.
None of the other
Correct option is C
 What does the
Bayesian network provides?
 Complete description
of the domain
 Partial description
of the domain
 Complete description
of the problem
 None of the mentioned
Correct option is C
 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
 How many terms are
required for building a Bayes model?
A.
2
B.
3
C.
4
D.
1
Correct option is B
 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
 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
 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
 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
 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
 How the Bayesian
network can be used to answer any query?
A.
Full distribution
B.
Joint distribution
C.
Partial distribution
D.
All of the mentioned
Correct option is B
 Sample Complexity is
A.
The sample complexity is the number of trainingsamples
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
 PAC stands for
A.
Probability Approximately Correct
B.
Probability Applied Correctly
C.
Partition Approximately Correct
Correct option is A
 Which of the
following will be true about k in kNN 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
 Which of the following
option is true about kNN 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
Answer
Correct option is C
 In kNN 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
 1
 2
 1 and 2
 None of these
Correct option is C
 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
 Which of the
following will be true about k in kNN 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
 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
 A radial basis
function is a
A.
Activation function
B.
Weight
C.
Learning rate