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 Absolute Error
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Root Mean Square Error
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Precision
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Recall
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Detailed explanation-1: -RMSE is a popular evaluation metric for regression problems because it not only calculates how close the prediction is to the actual value on average, but it also indicates the effect of large errors. Large errors will have an impact on the RMSE result.
Detailed explanation-2: -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-3: -Following are the performance metrics used for evaluating a regression model: Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE)
Detailed explanation-4: -The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models.
Detailed explanation-5: -Evaluation metrics for a linear regression model. Evaluation metrics are a measure of how good a model performs and how well it approximates the relationship. Let us look at MSE, MAE, R-squared, Adjusted R-squared, and RMSE.