Root Mean Square Error (RMSE) is a standard way to measure the error of a model in predicting quantitative data. It represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences.
Understanding RMSE in Practice
RMSE is particularly useful in regression analysis to verify experimental results. The lower the RMSE value, the closer predicted and observed values are, indicating a better model fit. It's sensitive to outliers, which can be both a strength and a weakness depending on the context of the analysis.
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