Mean Squared Error
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Mean Squared Error
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SUBOPTIMaL Mean Squared Error MSE
In Statistics Mean Squared Error MSE is defined as Mean or Average of the square of the difference between actual and estimated values Contributed by Swati Deval To understand it better let us take an example of actual demand and forecasted demand for a brand of ice creams in a shop in a year 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
Machine Learning Formulas Explained This Is The Formula For Mean
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