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ML MCQ all 5 - Machine Learning MCQ's
Machine Learning Techniques (KCS 052)
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Unit-
- 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
Answer Correct option is D
- Father of Machine Learning (ML) (A) Geoffrey Chaucer (B) Geoffrey Hill (C) Geoffrey Everest Hinton (D) None of the above
Answer Correct option is C
- 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
Answer Correct option is C
- 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
Answer Correct option is D
- 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
Answer
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 Answer Correct option is D
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 Answer Correct option is E
Which of the following does not include different learning methods (A) Analogy (B) Introduction (C) Memorization (D) Deduction Answer Correct option is B
In language understanding, the levels of knowledge that does not include? (A) Empirical (B) Logical (C) Phonological (D) Syntactic Answer Correct option is A
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
Answer Correct option is E
- Concept learning inferred a ______ valued function from training examples of its input and output.
(A) Supervised Learning: Classification (B) Reinforcement Learning (C) Unsupervised Learning: Clustering (D) Unsupervised Learning: Regression Answer Correct option is B
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 Answer Correct option is B
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
Answer Correct option is B
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 Answer Correct option is B
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
Answer Correct option is C
- 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
Answer Correct option is B
FIND-S algorithm ignores _______ examples. (A) Negative (B) Positive (C) Both (D) None of the above Answer Correct option is A
The Candidate-Elimination Algorithm represents the _____. (A) Solution Space (B) Version Space (C) Elimination Space (D) All of the above Answer Correct option is B
Inductive learning is based on the knowledge that if something happens a lot it is likely to be generally. (A) True (B) False Answer Correct option is A
Inductive learning takes examples and generalizes rather than starting with __________ knowledge. (A) Inductive (B) Existing (C) Deductive (D) None of these Answer Correct option is B
A drawback of the FIND-S is that, it assumes the consistency within the training set. (A) True (B) False Answer
(C) A double layer auto-associative neural network (D) An auto-associative neural network Answer Correct option is A
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 Answer Correct option is C
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 Answer Correct option is D
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 Answer Correct option is C
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 Answer Correct option is D
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 Answer Correct option is A
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 Answer Correct option is A
The backpropagation law is also known as generalized delta rule. (A) True (B) False Answer Correct option is A
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 Answer Correct option is A
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 Answer Correct option is D
There is feedback in final stage of backpropagation algorithm.
Answer Correct option is D
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 Answer Correct option is C
Neural Networks are complex _____ functions with many parameters. (A) Linear (B) Non linear (C) Discreate (D) Exponential
Answer Correct option is A
The general tasks that are performed with backpropagation algorithm (A) Pattern mapping (B) Prediction (C) Function approximation (D) All of the above Answer Correct option is D
Backpropagaion learning is based on the gradient descent along error surface. (A) True (B) False Answer Correct option is A
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 Answer Correct option is B
Applications of NN (Neural Network) (A) Risk management (B) Data validation (C) Sales forecasting (D) All of the above Answer Correct option is D
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 Answer Correct option is A
Decision Tree is a display of an algorithm. (A) True (B) False Answer Correct option is A
Which of the following is/are the decision tree nodes? (A) End Nodes (B) Decision Nodes (C) Chance Nodes (D) All of the above Answer Correct option is D
End Nodes are represented by which of the following (A) Solar street light (B) Triangles (C) Circles (D) Squares Answer Correct option is B
Decision Nodes are represented by which of the following (A) Solar street light (B) Triangles (C) Circles (D) Squares Answer Correct option is D
Unit-
- _____ terms are required for building a bayes model. (A) 1 (B) 2 (C) 3 (D) 4
Answer Correct option is C
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 Answer Correct option is A
Why it is needed to make probabilistic systems feasible in the world? (A) Feasibility (B) Reliability (C) Crucial robustness (D) None of the above Answer Correct option is C
Bayes rule can be used for:- (A) Solving queries (B) Increasing complexity (C) Answering probabilistic query (D) Decreasing complexity Answer Correct option is C
_____ 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 Answer Correct option is A
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 Answer Correct option is C
Probability provides a way of summarizing the ______ that comes from our laziness and ignorances. (A) Belief (B) Uncertaintity (C) Joint probability distributions (D) Randomness Answer Correct option is B
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 Answer Correct option is C
Causal chain (For example, Smoking cause cancer) gives rise to:- (A) Conditionally Independence (B) Conditionally Dependence (C) Both (D) None of the above Answer Correct option is A
The bayesian network can be used to answer any query by using:- (A) Full distribution (B) Joint distribution (C) Partial distribution (D) All of the above Answer Correct option is B
Bayesian networks allow compact specification of:- (A) Joint probability distributions (B) Belief (C) Propositional logic statements (D) All of the above Answer
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
Answer Correct option is C
Examples of Naïve Bayes Algorithm is/are (A) Spam filtration (B) Sentimental analysis (C) Classifying articles (D) All of the above Answer 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 fractions. (A) True (B) False Answer Correct option is A
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 Answer Correct option is A
Types of Naïve Bayes Model: (A) Gaussian (B) Multinomial (C) Bernoulli (D) All of the above Answer Correct option is D
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. Answer Correct option is A
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 Answer Correct option is D
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 Answer Correct option is B
Full form of MDL. (A) Minimum Description Length (B) Maximum Description Length (C) Minimum Domain Length (D) None of these Answer Correct option is A
Correct option is D
6. 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
Answer
Correct option is A
7. 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
Answer
Correct option is B
8. 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
Answer
Correct option is C
9. (For question no. 9 and 10) Can we say that concept described by
conjunctions of Boolean literals are PAC learnable?
(A) Yes
(B) No
Answer
Correct option is A
10. 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
Answer
Correct option is A
11. 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 H
(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.
Answer
Correct option is A
12. For a particular learning task, if the requirement of error parameter changes
from 0 to 0. How many more samples will be required for PAC learning?
(A) Same
(B) 2 times
(C) 1000 times
(D) 10 times
Answer
Correct option is D
13. 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
Answer
Correct option is D
14. The instance-based learner is a ____________
(A) Lazy-learner
(B) Eager learner
(C) Can’t say
Answer
Correct option is A
15. When to consider nearest neighbour algorithms?
(A) Instance map to point in kn
(B) Not more than 20 attributes per instance
ML MCQ all 5 - Machine Learning MCQ's
Course: Machine Learning Techniques (KCS 052)
University: Dr. A.P.J. Abdul Kalam Technical University
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