Table 4.

The mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and area under curve (AUC) of receiver operating characteristics (ROC) of the applied machine learning (ML) regression methods in predicting the grain yield per plant of the Lallemantia iberica ecotypes with the training of other traits, using the K-fold cross-validation as the data splitting method.

ML method	AUC-ROC	MAE	MSE	RMSE
Linear Regression	0.9667	0.010	0.001	0.018
Support vector regression (SVR); linear kernel	0.9302	0.038	0.002	0.048
SVR; Gaussian kernel	0.8775	0.069	0.007	0.086
SVR; polynomial kernel	0.8862	0.070	0.007	0.086
SVR; sigmoid kernel	0.3099	4.346	33.50	5.788
Random forest regression	0.9372	0.031	0.002	0.044
Gradient boosting decision tree regression	0.9503	0.030	0.002	0.040