# Questions on Regression [with answers]

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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 ðŸ‘‰:

- 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?

It is a technique to predict values

It is a technique to fix data

It is a Machine Learning algorithm

It is a technique to find outliers

Regression is used to create a relationship between a dependent variable to a one or more independent variables.

## Question 2

#### What is a dependent variable

The value we want to predict

The features of our dataset

The parameters of the regression algorithm

The values that interfere in the value we want to predict

Dependent variable is the value we want to predict. Imagine that we want to know how much is the value of a house. It is the dependent variable and we have to consider itÂ´s size, neighborhood, how many rooms, how many bathroom, does it have a garden, among other variables. ItÂ´s value depends of all these informations, and that is why it is called Dependent Variable.

## Question 3

#### What are independent variables?

The values that interfere in the value we want to predict

The features of our dataset

The parameters of the regression algorithm

The value we want to predict

Following the house price example, all variables that can interfere in the house price can be called as independent variable. House size, neighborhood, how many rooms, when it was built, etc.

## Question 4

#### What are outliers?

Extreme datapoints in our dataset

Values that are correlated to eachother

It is the main trend of our dataset

It is a regression technique

Outliers are extreme datapoints in our dataset that have too much more or less value than other datapoints. Most of the times outliers can be excluded from the dataset in order to preserve the regression quality. On the other hand, if we are working to prevent fraud, outliers is what we will be looking for, since their represent suspicious behavior.

## Question 5

#### What is Multicollinearity?

High correlation between independent variables

Low correlation between independent variables

Correlation between outliers

Correlation between features

When we have 2 or more independent variables with high correlation, we call it of Multicollinearity. It can be harmful to our regression because make harder to ranking the variables in order to know which one interfere more in our dependent variable. In these cases, we usually keep only one of those variables and discard the others.

## Question 6

#### What is overfitting?

Great result in training and poor result in test

Great result in training and great result in test

Poor result in training and poor result in test

Poor result in training and poor result in test

When we use unnecessary explanatory variables it might lead to overfitting.

## Question 7

#### What are Linear and Logistic regression?

There are types of regression

It is how you can classify a regression

A regression must be Linear or Logistic

There are types of overfitting

Although they are the most known types of Regression, there are many others.

## Question 8

#### Which answer explains better Linear Regression?

Dependent variable is continuous, independent variable(s) can be continuous or discrete, and nature of regression line is linear.

Dependent variable is discrete, independent variable(s) can be continuous or discrete, and nature of regression line is linear.

Dependent variable is continuous, independent variable(s) can be continuous or discrete, and nature of regression line is non-linear.

Dependent variable is discrete, independent variable(s) can be continuous or discrete, and nature of regression line is non-linear.

Linear Regression creates a relation between the dependent variables and all the independent variables using a best fit straight line, known as regression line.

## Question 9

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

When the dependent variable is binary

When the independent variables are binary

When the dependent variable is not binary

When the independent variables are not binary

Logistic Regression is used when we are looking for a binary value. This type of regression calculates the probability of a event has success or failure. It is widely used for classification.

## Question 10

#### For what Polynomial Regression is used?

Handle with non-linear and separable data

Handle linear and separable data

Classify binary data

Find the best linear line

When we handle with non-linear and separable data, a straight line will not work. In this case, Polynomial is very usefull and their always have independents variables with power higher than 1.

## Question 11

#### When we use Ridge Regression?

When our data have multicollinearity

When our data doens`t have multicollinearity

When we have a lot of outliers

When there is no outlier

Ridge Regression have a regularization parameter to fix the multicollinearity problem. It shrinks the value of coefficients but doesnâ€™t reaches zero, which suggests no feature selection feature.

## Question 12

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

It uses absolute values in regularization parameter, instead of squares

It uses square values in regularization parameter

It works better in small datasets

It works better in big datasets

Lasso Regression uses absolute values in the penalty function, instead of squares. The result is penalize values which causes some of the parameter estimates to turn out excactly zero.

## Question 13

#### What is ElasticNet Regression?

It is a mix of Lasso and Ridge Regression

It is the newest type of regression

It is the best way to use regression in Machine Learning

It is a type of regression focused in outliers

It is a combination of L1 and L2 regularization.

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