Question 9In Linear Regression, which statement is correct about Sum Squared Error?1 pointThe Sum Squared Error measures the distance between the truth and predicted values.The Sum Squared Error measures the distance between the truth and the average values of the truth.The Sum Squared Error is a measure of the explained variation of our model.The Sum Squared Error measures the distance between the predicted values and the average values of the truth.
Question
Question 9In Linear Regression, which statement is correct about Sum Squared Error?1 pointThe Sum Squared Error measures the distance between the truth and predicted values.The Sum Squared Error measures the distance between the truth and the average values of the truth.The Sum Squared Error is a measure of the explained variation of our model.The Sum Squared Error measures the distance between the predicted values and the average values of the truth.
Solution
The correct statement about Sum Squared Error in Linear Regression is: "The Sum Squared Error measures the distance between the truth and predicted values."
Here's why:
Sum Squared Error (SSE) is a common metric used in statistical analysis to measure the total deviation of the response values from the predicted values in a regression model. It is calculated as the sum of the squared differences between the actual (truth) and the predicted values.
So, the statement "The Sum Squared Error measures the distance between the truth and predicted values" is correct.
The other statements are not accurate descriptions of what the Sum Squared Error measures in the context of Linear Regression.
Similar Questions
Q.No.1. The _________ metric measures the squared difference between predicted and actualvalues in linear regression
The linear regression model y = a0 + a1x is applied to the data in the table shown below. What is the value of the sum squared error function S(a0, a1), when a0 = 1, a1 = 2? A. 0.0 B. 27 C. 13.5 D. 54
What does SSE (Sum of Squared Errors) represent in the context of regression analysis?
The method of least squares calculates square of deviations of the points from the line chosen using Linear Regression. Our target is that this error should be
In comparison to mean absolute error, mean squared error:1 pointIs more interpretable by taking the same unit as the response.Focuses more on large errors.Weighs small and large errors equally.Avoids cancellation of errors.
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