COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Mode Squared Error
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Median Squared Error
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Mean Squared Error
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Mass Squared Error
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Detailed explanation-1: -In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors-that is, the average squared difference between the estimated values and the actual value.
Detailed explanation-2: -In statistics, the mean squared error (MSE) is a risk function that measures the square of errors.
Detailed explanation-3: -Mean squared error (MSE) measures the amount of error in statistical models. It assesses the average squared difference between the observed and predicted values. When a model has no error, the MSE equals zero. As model error increases, its value increases.
Detailed explanation-4: -The mean squared error (MSE) of this estimator is defined as E[(X−ˆX)2]=E[(X−g(Y))2]. The MMSE estimator of X, ˆXM=E[X|Y], has the lowest MSE among all possible estimators.
Detailed explanation-5: -MSE is used to check how close estimates or forecasts are to actual values. Lower the MSE, the closer is forecast to actual. This is used as a model evaluation measure for regression models and the lower value indicates a better fit.