Question 2Which of the following statements comparing linear and logistic regressions is TRUE?1 pointIn this course, linear regression minimizes the mean absolute error, while logistic regression minimizes the mean squared error.Independent variables in linear regression can be continuous or categorical, but can only be categorical in logistic regression.Linear regression is used for a continuous target whereas logistic regression is more suitable for a categorical target.Both linear and logistic regression can be used to predict categorical responses and attain a point’s likelihood of belonging to each class.
Question
Question 2Which of the following statements comparing linear and logistic regressions is TRUE?1 pointIn this course, linear regression minimizes the mean absolute error, while logistic regression minimizes the mean squared error.Independent variables in linear regression can be continuous or categorical, but can only be categorical in logistic regression.Linear regression is used for a continuous target whereas logistic regression is more suitable for a categorical target.Both linear and logistic regression can be used to predict categorical responses and attain a point’s likelihood of belonging to each class.
Solution
The true statement comparing linear and logistic regressions is: "Linear regression is used for a continuous target whereas logistic regression is more suitable for a categorical target."
Here's why:
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Both linear and logistic regression minimize the mean squared error, not the mean absolute error. So, the first statement is incorrect.
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Independent variables in both linear and logistic regression can be continuous or categorical. So, the second statement is incorrect.
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Linear regression is indeed used for a continuous target, and logistic regression is used when the target is categorical. So, the third statement is correct.
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While it's true that both linear and logistic regression can be used to predict categorical responses, linear regression isn't typically used for this purpose because it can predict values outside the range of 0 and 1, which doesn't make sense for categorical variables. So, the fourth statement is incorrect.
Similar Questions
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Predicter Models (10 marks):a) Name and explain 2 major differences between linear regression and logistic regression.b) Name and explain 3 features linear regression and logistic regression have in common.
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