Root Mean square error (RMSE) is one of the most commonly used loss functions for regression problems. One way to assess how well a regression model fits a dataset is to calculate the root mean square error, RMSE is the standard deviation of the residuals. Residuals are a measure of how far from the regression line data points are. Residuals are nothing but prediction errors, we can find it by subtracting the predicted value from the actual value.
it can be defined mathematically as,
The lower the RMSE, the better a given model is able to “fit” a dataset. RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit.
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