The term root mean square error (RMSE) is the square root of mean squared error (MSE). RMSE measures the differences between values predicted by a hypothetical model and the observed values. In other words, it measures the quality of the fit between the actual data and the predicted model. RMSE is one of the most frequently used measures of the goodness of fit of generalized regression models.
In the application of regression models, unless the relationship or correlation is perfect, the predicted values are more or less different from the actual observations. These differences are prediction errors or residuals. These residuals are measured by the vertical distances between the actual values and the regression line. Large distances are indicative of large errors. However, for ...
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