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**Practice multiple choice questions on Regression** with answers. This is one of the fundamental techniques in Machine Learning which is widely used in basic problems.

If you want to revise the concept, read this article ๐:

- Summary of Regression Techniques
- What is Linear Regression?
- Logistic Regression
- Advantages and Disadvantages of Linear Regression

Let us start with the questions. Click on the right option and the answer will be explained.

## Question 1

#### What is Regression?

## Question 2

#### What is a dependent variable

## Question 3

#### What are independent variables?

## Question 4

#### What are outliers?

## Question 5

#### What is Multicollinearity?

## Question 6

#### What is overfitting?

## Question 7

#### What are Linear and Logistic regression?

## Q8. Linear Regression is an example of?

a) Supervised Learning

b) Unsupervised Learning

c) Semi-Supervised Learning

Answer: a) Supervised Learning

Linear Regression is an example of Supervised Learning. In fact, as a rule, all Regression techniques are an example of Supervised Learning.

## Q9. Logistic Regression is an example of?

a) Supervised Learning: Regression

b) Classification

b) Unsupervised Learning

c) Semi-Supervised Learning

Answer: b) Classification

Logistic Regression is an example of Classification which is a type of Supervised Learning. As mentioned previously, all Regression techniques are an example of Supervised Learning. Exception is that Logistic Regression is not counted as a regression technique but as a Classification technique.

## Question 10

#### Which answer explains better Linear Regression?

## Question 11

#### When is appropriate to use Logistic Regression?

## Question 12

#### For what Polynomial Regression is used?

## Question 13

#### When we use Ridge Regression?

## Q14. Which regression is used in the following image?

a) Linear Regression

b) Logistic Regression

c) Polynomial Regression

d) Ridge Regression

Answer: c) Polynomial Regression

Multiple curves in a line denote the graph is of a polynomial of multiple degree and hence, it is using Polynomial Regression.

## Question 15

#### Which way Lasso Regression differs from Ridge Regression?

## Question 16

#### What is ElasticNet Regression?

## Q17. Consider the following image. Which one is used for Low Bias Low Variance?

a) Ridge Regression

b) Lasso Regression

c) Elastic Net Regression

d) Linear Regression

Answer: c) Elastic Net Regression

Ridge and Lasso Regression is used for high bias and high variance.

The scenario we are looking for is with Low Bias and Low Variance in order to have a better prediction from our model. Then we use regularization to reduce our variance and introducing some Bias.

We already did that using Ridge and Lasso, but both of them has faults. Elastic Net was created to combine the penalties of ridge regression and lasso to get the best of both worlds. Elastic Net aims at minimizing the loss information.

## Q18. Which Regression technique uses F-test or T-test?

a) Ridge Regression

b) Stepwise Regression

c) Elastic Net Regression

d) Linear Regression

Answer: b) Stepwise Regression

Stepwise regression is a technique which adds or removes variables via series of F-tests or T-tests. The variables to be added or removed are chosen based on the test statistics of the estimated coefficients.

With these questions on Regression at OpenGenus, you must have a good idea of Regression. Enjoy.