This set of Machine Learning Multiple Choice Questions & Answers (MCQs) focuses on “Logistic Regression”.

1. What kind of algorithm is logistic regression?

a) Cost function minimization

b) Ranking

c) Regression

d) Classification

View Answer

Explanation: Logistic regression is a classification problem. The target variable is categorical (specific few options). Logistic regression outputs in yes or no / true or false / 0 or 1 and so on.

2. Can a cancer detection problem be solved by logistic regression?

a) Sometimes

b) No

c) Yes

d) Depends on the dataset

View Answer

Explanation: If the target is to detect cancer, logistic regression can always be used. Logistic regression algorithm will output if the patient has cancer or not, depending on the symptoms and training examples.

3. In a logistic regression problem, there are 300 instances. 270 people voted. 30 people did not cast their votes. What is the probability of finding a person who cast one’s vote?

a) 10%

b) 90%

c) 0.9

d) 0.1

View Answer

Explanation: 270 out of 300 people voted. Hence, the probability of finding a person who cast his/her vote is 270/300 or 9/10 i.e. 0.9. Since probability has to be within 0 or 1, it can never be 90%.

4. In a logistic regression problem, what is a possible output for a new instance?

a) 0.85

b) -0.19

c) 1.20

d) 89%

View Answer

Explanation: The output in a logistic regression problem is calculated by a probability function. Thus, the output can only be between 0 and 1. It cannot be negative, or greater than 1. It is not expressed in a percentage.

5. The output in a logistic regression problem is yes (equivalent to 1 or true). What is its possible value?

a) Greater than 0.5

b) Depends on the algorithm’s threshold value

c) Greater than 0.6

d) Equal to 1

View Answer

Explanation: If the output is true, the probability of the instance to be true is greater than the threshold value. Now, for different datasets, the threshold value can be different. It can be 0.5, it can also be 0.6. It is dependent on the algorithm.

6. Who invented logistic regression?

a) Vapnik

b) Ross Quinlan

c) DR Cox

d) Chervonenkis

View Answer

Explanation: Statistician DR Cox invented Logistic Regression in 1958. Ross Quinlan is the founder of the machine learning model decision tree. Vapnik and Chervonenkis introduced the idea of VC dimension.

7. An artificially intelligent car knows if to brake or not based on its distance from the car in front of it. Logistic regression algorithm is used.

a) True

b) False

View Answer

Explanation: The output is given as yes or no, based on the distance from the car in front of it. It is thus a classification problem. Hence, the logistic regression algorithm can be used to determine whether to stop or not.

8. An artificially intelligent car decreases its speed based on its distance from the car in front of it. Which algorithm is used?

a) Decision Tree

b) Naïve-Bayes

c) Logistic Regression

d) Linear Regression

View Answer

Explanation: The output is numerical. It determines the speed of the car. Hence it is not a classification problem. All the three, decision tree, naïve-Bayes, and logistic regression are classification algorithms. Linear regression, on the other hand, outputs numerical values based on input. So, this can be used.

9. In a logistic regression problem an instance is similar to 60 positive instances, 20 negative instances, dissimilar to 30 positive instances, 90 negative instances. What kind of an instance is this?

a) Negative instance

b) Positive instance

c) Cannot be determined, even if the threshold is given

d) Can be determined, if the threshold is given

View Answer

Explanation: Similarity or dissimilarity does not determine the output of logistic regression. The output is completely dependent on the independent variables and their values. So, the output cannot be determined even if the threshold is given.

10. When was logistic regression invented?

a) 1968

b) 1958

c) 1948

d) 1988

View Answer

Explanation: Logistic regression was invented by statistician DR Cox in the year 1958. It was introduced even before the invention of machine learning. It was introduced as a part of the direct probability model.

**More MCQs on Logistic Regression:**

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