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Table 2 Equation of performance evaluation metrics

From: Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients

Metrics

Description

Calculation

SMAPE

Symmetric Mean Absolute Percentage Error

\(SMAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|{y}_{i}-{\widehat{y}}_{i}\right|}{\left(\left|{y}_{i}\right|+\left|{\widehat{y}}_{i}\right|\right)/2}\times 100\)

MAPE

Mean Absolute Percentage Error

\(MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{{y}_{i}-{\widehat{y}}_{i}}{{y}_{i}}\right|\times 100\)

RAE

Relative Absolute Error

\(RAE=\sqrt{\frac{{\sum }_{i=1}^{n}{\left({y}_{i}-{\widehat{y}}_{i}\right)}^{2}}{{\sum }_{i=1}^{n}{\left({y}_{i}\right)}^{2}}}\)

RRSE

Root Relative Squared Error

\(RRSE=\sqrt{\frac{\sum_{i=1}^{n}{\left({y}_{i}-{\widehat{y}}_{i}\right)}^{2}}{\sum_{i=1}^{n}{\left({y}_{i}-{\widehat{y}}_{i}\right)}^{2}}}\)

RMSE

Root Mean Squared Error

\(RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{\left({y}_{i}-{\widehat{y}}_{i}\right)}^{2}}\)

  1. where \({\widehat{y}}_{i}\) and \({y}_{i}\) represent predicted and actual values, respectively; \(n\) stands the number of instances