# Machine Learning Questions and Answers – Error Correcting Output Codes

This set of Machine Learning Multiple Choice Questions & Answers (MCQs) focuses on “Error Correcting Output Codes”.

1. In error-correcting output codes (ECOC), the main classification task is defined in terms of a number of subtasks that are implemented by the base-learners.
a) True
b) False
View Answer

Answer: a
Explanation: In multi-class problems the original task of separating one class from all other classes may be difficult. So we want to define a set of simpler classification problems, where each specializing in one aspect of the task. And we get the final classifier by combining these simpler classifiers.

2. Which of the following statements is not true about error-correcting output codes (ECOC)?
a) It is a method for solving multi-class classification problems
b) It is a method for decomposing a multiway classification problem into many binary classification tasks
c) It is a method for solving binary classification problems
d) It is a method for converting a k-class supervised learning problem into a large number of two class supervised learning problem
View Answer

Answer: c
Explanation: Error-correcting output coding is not a method for solving binary classification problems. It is a method for solving multi-class classification problems. Here a k-class supervised learning problem (multiway classification problem) is converted into a large number L of two class supervised learning problems (binary classification tasks).

3. Which of the following statements is not true about multi-class classification?
a) An input can belong to one of K classes
b) Each input belongs to exactly one class
c) Each training data associated with class labels which is a number from 1 to K
d) Each input belongs to more than one class
View Answer

Answer: d
Explanation: In a multi-class classification problem, an input cannot belong to more than one class. Here an input can belong to one of K classes, but each input belongs to exactly one class. And the training data input is associated with a class label (a number from 1 to K).
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4. Which of the following statements is false about error-correcting output codes (ECOC)?
a) It is based on the embedding of binary classifiers
b) The ECOC designs are independent of the base classifier applied
c) ECOC framework consists of designing a codeword for each of the classes
d) The ECOC designs are dependent on the base classifier applied
View Answer

Answer: d
Explanation: ECOC designs are not dependent on the base classifier applied and are independent of the base classifier applied. ECOC is the powerful framework based on the embedding of binary classifiers. It consists of designing a codeword for each of the classes and these codewords encode the membership information of each class for a given binary problem.

5. Which of the following is not a problem independent ECOC design?
a) One-versus-all
b) SFFS criterion
c) One-versus-one
d) Dense Random
View Answer

Answer: b
Explanation: Sequential Forward Floating Search (SFFS) is not a problem independent ECOC design. It is a method used in problem dependent ECOC design for feature selection. And all other three are the problem independent ECOC designs.

6. Which of the following is not a problem dependent ECOC design?
a) Sparse Random
b) DECOC
c) ECOC-ONE
d) Forest-ECOC
View Answer

Answer: a
Explanation: Sparse Random is not a problem dependent ECOC design and it is a problem independent ECOC design. It uses n = 15 · logNc dichotomizers. All other three are the problem dependent ECOC designs.

7. Which of the following ECOC designs uses n = (Nc−1).T dichotomizers, where T stands for the number of binary tree structures to be embedded?
a) DECOC
b) One-versus-all
c) Forest-ECOC
d) One-versus-one
View Answer

Answer: c
Explanation: Forest-ECOC design uses n = (Nc−1).T dichotomizers, where T stands for the number of binary tree structures to be embedded. Whereas the DECOC design uses n = Nc−1 dichotomizers, One-versus-all uses Nc dichotomizers and One-versus-one uses n = Nc(Nc−1)/2 dichotomizers.
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8. Forest-ECOC design which uses n = (Nc−1).T dichotomizers, extends the variability of the classifiers of the DECOC design.
a) True
b) False
View Answer

Answer: a
Explanation: Forest-ECOC design uses n = (Nc−1).T dichotomizers, where T stands for the number of binary tree structures to be embedded. Whereas the DECOC design uses n = Nc−1 dichotomizers. So Forest-ECOC design extends the variability of the classifiers of the DECOC design by including extra dichotomizers T (number of binary tree structures to be embedded).

9. Problem independent ECOC design and Problem dependent ECOC design are the two types of ECOC decoding strategies.
a) True
b) False
View Answer

Answer: b
Explanation: Problem independent ECOC design and Problem dependent ECOC design are not the two types of ECOC decoding strategies. The ECOC coding designs are mainly divided into two main groups: problem-independent approaches, and the problem-dependent designs. Hamming decoding, Euclidean decoding etc. are the ECOC decoding strategies.
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10. Problem independent approaches take into account the distribution of the data to define the coding matrix.
a) False
b) True
View Answer

Answer: a
Explanation: Problem-independent approaches are used to guide the coding design. It does not take into account the distribution of the data to define the coding matrix. It considers the row separation and column separation criteria to build a code matrix.

11. Given the two strings “cats” and “dogs”. What is the Hamming distance between two strings?
a) 4
b) 3
c) 2
d) 5
View Answer

Answer: b
Explanation: The Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different. It is the number of substitutions required to transform one string into another.
cats ⇒ dats (substitute ‘d’ for ‘c’)
dats ⇒ dots (substitute ‘o’ for ‘a’)
dots ⇒ dogs (substitute ‘g’ for ‘t’)
So hamming distance is 3 as it requires 3 edit operations to convert “cats” to “dogs”.

12. What is the hamming distance between the binary values 100111010 and 101111111?
a) 9
b) 7
c) 5
d) 3
View Answer

Answer: d
Explanation: Hamming distance between two strings of equal length is the minimum number of substitutions required to change one string into the other.
100111010 ⇒ 101111010
101111010 ⇒ 101111110
101111110 ⇒ 101111111
So hamming distance is 3 as it requires 3 edit operations to convert 100111010 to 101111111.

13. How many single bit errors take to turn “cow” to “fox”?
a) 2
b) 0
c) 1
d) 3
View Answer

Answer: a
Explanation: The number of single bit errors taken to turn one string into another is known as the hamming distance.
cow ⇒ fow (substitute ‘f’ for ‘c’)
fow ⇒ fox (substitute ‘x’ for ‘w’)
So the number of single bit errors taken to turn “cow” to “fox” is 2.

Sanfoundry Global Education & Learning Series – Machine Learning.

To practice all areas of Machine Learning, here is complete set of 1000+ Multiple Choice Questions and Answers.

If you find a mistake in question / option / answer, kindly take a screenshot and email to [email protected]

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