The ROC index is not a useful error measure to compare regressions. The ROC (Receiver Operating Characteristic) curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. It tells how much model is capable of distinguishing between classes. However, it is not used for regression models.

On the other hand, SSE (Sum of Squared Errors), R squared, and TSS (Total Sum of Squares) are all common measures used to evaluate the performance of regression models.

SSE measures the total discrepancy between the data and an estimation model. A small SSE indicates a tight fit of the model to the data.

R squared, also known as the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model.

TSS is the total variance in the response Y, and can be thought of as the amount of variability inherent in the response before the regression is performed.

In contrast, the ROC index is used for evaluating classification models, not regression models.

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The ROC index is not a useful error measure to compare regressions. The ROC (Receiver Operating Characteristic) curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. It tells how much model is capable of distinguishing between classes. However, it is not used for regression models.

On the other hand, SSE (Sum of Squared Errors), R squared, and TSS (Total Sum of Squares) are all common measures used to evaluate the performance of regression models.

SSE measures the total discrepancy between the data and an estimation model. A small SSE indicates a tight fit of the model to the data.

R squared, also known as the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model.

TSS is the total variance in the response Y, and can be thought of as the amount of variability inherent in the response before the regression is performed.

In contrast, the ROC index is used for evaluating classification models, not regression models.

Knowee AI · Accepted Answer

The ROC index is not a useful error measure to compare regressions. The ROC (Receiver Operating Characteristic) curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. It tells how much model is capable of distinguishing between classes. However, it is not used for regression models.

On the other hand, SSE (Sum of Squared Errors), R squared, and TSS (Total Sum of Squares) are all common measures used to evaluate the performance of regression models.

SSE measures the total discrepancy between the data and an estimation model. A small SSE indicates a tight fit of the model to the data.

R squared, also known as the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model.

TSS is the total variance in the response Y, and can be thought of as the amount of variability inherent in the response before the regression is performed.

In contrast, the ROC index is used for evaluating classification models, not regression models.

Question 6All of these options are useful error measures to compare regressions except:1 pointSSER squaredTSSROC index

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