COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Mean squared error (MSE)
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Confusion Matrix
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Sensitivity, Specificity, Accuracy
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Receiver Operating Characteristics (ROC) Curve
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Detailed explanation-1: -The Mean Squared Error measures the average of the errors squared. It basically calculates the difference between the estimated and the actual value, squares these results and then computes their average.
Detailed explanation-2: -D. All of the above. These (R Squared, Adjusted R Squared, F Statistics, RMSE / MSE / MAE ) are some metrics which you can use to evaluate your regression model.
Detailed explanation-3: -Root Mean Squared Error (RMSE) RMSE is the most famous evaluation metric for the regression model. The overall calculation of RMSE is similar to MSE; the final value is square-rooted as we calculated the square of errors in MSE.
Detailed explanation-4: -R-Squared: seldom used for evaluating model fit.