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