Regression algorithms-driven mechanical properties prediction of angle bracket connection on cross-laminated timber structures

Journal of Wood Science

Official Journal of the Japan Wood Research Society

Table 2 Evaluation metrics for the regression model

Evaluation metrics	Definition
MAE	\(MAE\left(y,\widehat{y}\right)=\frac{1}{n}\sum_{i=1}^{n}\left\|{y}_{i}-{\widehat{y}}_{i}\right\|\)
MSE	\(MSE\left(y,\widehat{y}\right)=\frac{1}{n}\sum_{i=1}^{n}{({y}_{i}-{\widehat{y}}_{i})}^{2}\)
RMSE	\(RMSE\left(y,\widehat{y}\right)=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{({y}_{i}-{\widehat{y}}_{i})}^{2}}\)
R²	\({R}^{2}\left(y,\widehat{y}\right)=1-\frac{\sum_{i=1}^{n}{({y}_{i}-{\widehat{y}}_{i})}^{2}}{\sum_{i=1}^{n}{({y}_{i}-{\overline{y} }_{i})}^{2}}\)

\(y\), \(\widehat{y}\) are the target value and the predicted value for the normalized test dataset, respectively; \(n\) is the number of samples in the test dataset; and \({y}_{i}\), \({\widehat{y}}_{i}\) refer to the i-th target value and the i-th predicted value for the normalized test dataset, respectively