# Logistic Regression MCQ Questions & Answers

Logistic Regression MCQs : This section focuses on "Basics" of Logistic Regression. These Multiple Choice Questions (MCQ) should be practiced to improve the Logistic Regression skills required for various interviews (campus interview, walk-in interview, company interview), placements, entrance exams and other competitive examinations.

1. Which of the following is used where the target variable is of categorical nature?

A. Keras

B. Knime

C. Logistic Regression

D. MXNet

View Answer

Ans : C

Explanation: It’s a classification algorithm that is used where the target variable is of categorical nature. The main objective behind Logistic Regression is to determine the relationship between features and the probability of a particular outcome.

2. How many different types of Logistic Regression?

A. 2

B. 3

C. 4

D. 5

View Answer

Ans : B

Explanation: Three different types of Logistic Regression are as follows: Binary Logistic Regression, Multinomial Logistic Regression and Ordinal Logistic Regression

3. _________ the target variable can have three or more possible values without any order.

A. Multinomial Logistic Regression

B. Binary Logistic Regression

C. Ordinal Logistic Regression

D. All of the above

View Answer

Ans : A

Explanation: Multinomial Logistic Regression: In this, the target variable can have three or more possible values without any order.

4. _______ are defined as the ratio of the probability of an event occurring to the probability of the event not occurring.

A. Simple

B. Even

C. Regex

D. Odds

View Answer

Ans : D

Explanation: Odds are defined as the ratio of the probability of an event occurring to the probability of the event not occurring.

5. SVM is insensitive to individual samples.

A. Yes

B. No

C. Can be yes or no

D. Can not say

View Answer

Ans : A

Explanation: Yes, SVM is insensitive to individual samples. So, to accommodate an outlier there will not be a major shift in the linear boundary. SVM comes with inbuilt complexity controls, which take care of overfitting, which is not true in the case of Logistic Regression.

6. Which of the following are advantages of the logistic regression?

A. Logistic Regression is very easy to understand

B. It requires less training

C. It performs well for simple datasets as well as when the data set is linearly separable

D. All of the above

View Answer

Ans : D

Explanation: All of the above are are the advantages of Logistic Regression

7. 0 and 1, or pass and fail or true and false is an example of?

A. Multinomial Logistic Regression

B. Binary Logistic Regression

C. Ordinal Logistic Regression

D. None of the above

View Answer

Ans : B

Explanation: Binary Logistic Regression: In this, the target variable has only two 2 possible outcomes. For Example, 0 and 1, or pass and fail or true and false.

8. Mean Square Error (MSE) is suitable for Logistic Regression.

A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

View Answer

Ans : A

Explanation: MSE is not suitable for Logistic Regression

9. What are the disadvantages of Logistic Regression?

A. Sometimes a lot of Feature Engineering is required

B. It is quite sensitive to noise and overfitting

C. Both A and B

D. None of the above

View Answer

Ans : C

Explanation: Both A and B are the disadvantages of Logistic Regression.

10. Can we solve the multiclass classification problems using Logistic Regression?

A. Yes

B. No

C. Can be yes or no

D. Can not say

View Answer

Ans : A

Explanation: Yes, in order to deal with multiclass classification using Logistic Regression, the most famous method is known as the one-vs-all approach.

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