LinearRegression

1. What is the primary objective of Linear Regression in machine learning?

• A) To find clusters

  in data

• B) To minimize the

  Euclidean distance between points

• C) To predict continuous values

  based on input features

• D) To classify data

  into distinct categories

2. In Linear Regression, what does the term "residual" refer to?

• A) The difference between the

  predicted value and the actual value

• B) The slope of the

  regression line

• C) The intercept of

  the regression line

• D) The total sum of squared errors

3. Which of the following is the cost function used in Linear Regression?

• A) Cross-Entropy Loss

• B) Mean Absolute Error

• C) Mean Squared Error

• D) Hinge Loss

4. In Linear Regression, the relationship between

input variables and output are assumed to be:

• A) Linear

• B) Exponential

• C) Polynomial

• D) Logarithmic

5. Which method is commonly used to

find the best-fit line in Linear Regression?

• A) Gradient Descent

• B) Decision Trees

• C) Random Forests

• D) Backpropagation

6. In simple linear regression, the equation of

the line is represented as y=mx+cy = mx + cy=mx+c.

What does "m" represent in this equation?

• A) The y-intercept

  of the line

• B) The residuals of

  the data points

• C) The slope

  of the line

• D) The output

  variable

7. Which of the following is NOT an

assumption of Linear Regression?

• A) Linearity of

  the relationship

• B) Homoscedasticity

• C) Multicollinearity

• D) Independence

  of errors

8. What is R-squared in the

context of Linear Regression?

• • A) A metric that evaluates

  • how well the model

  • fits the data

• B) A measure of the

  error rate of predictions

• C) The slope of the

  regression line

• D) The p-value of

  the model

9. If R-squared is equal to 1 in a

Linear Regression model, what does it indicate?

• A) The model does

  not fit the data well

• B) The model perfectly

  explains the

  variance in the data

• C) There is no linear

  relationship in the data

• D) The data is

  overfitting the model

10. Which of the following is TRUE about

Linear Regression with multiple variables?

• A) It is called

  simple linear regression

• B) It is known as

  multiple linear regression

• C) It is known as

  logistic regression

• D) It is not

  possible in linear regression