APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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If R Squared increases, this variable is significant.
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If R Squared decreases, this variable is not significant.
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Individually R squared cannot tell about variable importance. We can’t say anything about it right now.
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None of these
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Detailed explanation-1: -In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model. Which of the following option is true? If R Squared increases, this variable is significant.
Detailed explanation-2: -Consider a model where the R2 value is 70%. Here r squared meaning would be that the model explains 70% of the fitted data in the regression model. Usually, when the R2 value is high, it suggests a better fit for the model.
Detailed explanation-3: -R-squared does not measure goodness of fit. R-squared does not measure predictive error. R-squared does not allow you to compare models using transformed responses. R-squared does not measure how one variable explains another.
Detailed explanation-4: -What qualifies as a “good” R-Squared value will depend on the context. In some fields, such as the social sciences, even a relatively low R-Squared such as 0.5 could be considered relatively strong. In other fields, the standards for a good R-Squared reading can be much higher, such as 0.9 or above.