MACHINE LEARNING UNIT – 2 (B) NOTES (ARTIFICIAL NEURAL NETWORKS)

MACHINE LEARNING UNIT – 2 (A) PPT's (Decision Tree Learning)

MACHINE LEARNING UNIT - 1 NOTES ( Introduction, Concept Learning and the General to Specific Ordering)

MACHINE LEARNING UNIT - 1 PPT's (Introduction, Concept Learning and the General to Specific Ordering)

 

Bayes Theorem, Naive Bayes Algorithm, KNN Algorithm MCQs

1.     Formula for Bayes theorem is _______

Ans: A

2.     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

Ans: B

3.      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) None of the above

Ans: C

4.      Naïve Bayes Algorithm is a ________ learning algorithm.
(A) Supervised
(B) Reinforcement
(C) Unsupervised
(D) None of these

Ans: A

 

5.     Examples of Naïve Bayes Algorithm is/are
(A) Spam filtration
(B) Sentimental analysis
(C) Classifying articles
(D) All of the above

Ans: D

6.      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

Ans: A

7.     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.

Ans: A

8.     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

Ans: D

9. Identify the distance matrix used in KNN Algorithm___________

A.    Euclidean Distance.

B.    Cosine Similarity.

C.    Manhattan Distance.

D.    All.

Ans: D

10. ______in KNN is a parameter that refers to the number of nearest neighbors to include in the majority of the voting process.

A.    K.

B.    First N.

C.    Second N.

D.    All of the above.

Ans: A

 Genetic Algorithm MCQs

1.     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

Ans: C

2.     Genetic operators includes

(A) Crossover

(B) Mutation

(C) Both A & B

(D) None of these

Ans: C

3.     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)

      Ans: C

4. 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

Ans:  C

5.     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

(D) None of these

Ans:  A

6.     GA techniques are inspired by _________ biology.

(A) Evolutionary

(B) Cytology

(C)  Anatomy

(D)  Ecology

Ans: A

7.     _________ 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

Ans: B

8. ILP stand for

(A) Inductive Logical programming

(B) Inductive Logic Programming

(C) Inductive Logical Program

(D) Inductive Logic Program

Ans: B

9. 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) All of the above

Ans: D

10. _________ is any predicate (or its negation) applied to any set of terms.

(A) Literal

(B) Null

(C) Clause

(D) None of these

Ans: A

11. 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

 Ans: C

 Reinforcement Learning MCQs

1.     _________________ is a type of machine learning in which an agent learns to make decisions by interacting with an environment.

 

A. Supervised learning

B. Reinforcement learning

C. Semi-supervised learning

D. Unsupervised learning

Ans: B

 

2. Which of the following are applications of Reinforcement learning?

 

A. Robotics

B. video games

C. self-driving cars

D. All of the above

Ans: D

 3._________________ is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior.

 

A. Positive Reinforcement

B. Negative Reinforcement

C. Both A and B

D. None of the above

Ans : A

 

4. Which type of feedback does an agent in reinforcement learning receive?

A. Predictions
B. Labels
C. Clusters
D. Rewards or penalties

Ans: D

 5. ______________ is the Q-learning algorithm used for in reinforcement learning.

 

A. Image recognition

B. Clustering

C. Finding the optimal decision-making strategy

D. Natural language processing

Ans: C

 6. Which of the following are Advantages of reinforcement learning?

 

A. Maximizes Performance

B. Sustain Change for a long period of time

C. Too much Reinforcement can lead to an overload of states which can diminish the results

D. All of the above

Ans: D

 7.  In Reinforcement learning, the decision is made on the initial input or the input given at the start

  A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

 Ans : B

 8. Which of the following is true about reinforcement learning?

 A. The agent gets rewards or penalty according to the action

B.    The agent navigates in an environmental building the example experiences or the training dataset.

C.    The target of an agent is to maximize the rewards and minimize the penalty.

D.    All of the above

Ans: D

 9. Q-Learning algorithm is off-policy algorithm because

A.    Policy being trained is exactly the one being executed

B.    Policy being trained is not necessarily the one being executed.
There is nothing called Q-Learning algorithm but Q-Value Iteration

D.    None of the above

Ans: B

 10. In Reinforcement learning decision is dependent, So we give labels to sequences of dependent decisions.

 

A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

Ans: A