MACHINE LEARNING
INTRODUCTION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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MSE
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MEA
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Precision
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Recall
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Detailed explanation-1: -The closer your MSE value is to 0, the more accurate your model is. However, there is no ‘good’ value for MSE. It is an absolute value which is unique to each dataset and can only be used to say whether the model has become more or less accurate than a previous run.
Detailed explanation-2: -So a robust system or metric must be less affected by outliers. In this scenario it is easy to conclude that MSE may be less robust than MAE, since the squaring of the errors will enforce a higher importance on outliers.
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.