#Community quiz (Linear regression)

Question 1: Which one is the correct Linear regression assumption?
(A) Linear regression assumes the input and output variables are not noisy
(B) Linear regression will over-fit your data when you have highly correlated input variables
(C) The residuals (true target value − predicted target value) of the data are normally distributed and independent from each other
(D) All of the above

Question 2: For a Linear Regression model, we choose the coefficients and the bias term by minimizing the _____.
(A) Loss function
(B) Error function
(C) Cost function
(D) All of the above

Question 3: Which parameter determines the size of the improvement step to take on each iteration of Gradient Descent?
(A) learning rate
(B) epoch
(C) batch size
(D) regularization parameter

Question 4: In a simple linear regression problem, a single input variable (x) and a single output variable (y), the linear equation would be y = ax + b; where a and b are _______ and ________ respectively.
(A) bias Coefficient, feature coefficient
(B) feature coefficient, bias Coefficient
(C) slope, y-intercept
(D) y-intercept, slope

Question 5: In a linear regression model, which technique can find the coefficients?
(A) Ordinary Least Squares
(B) Gradient Descent
(C) Regularization
(D) All of the above

1 Like

Linear Regression

  1. . D
  2. . D
  3. . A
  4. . B,C
  5. . D

Liner Regression
1 - C 2 - C 3 - A 4 - C 5 - D