Several statistical analyses are performed, such as Adjusted R2 score, NRMSE, MAD, MSE and MAE to examine the performance of different algorithm. SHAP and LIME are examples of XAI that is responsible for interpreting and understanding the reasons behind getting specific results. The main organization...
The last two columns provide the root mean square error (RMSE) and mean absolute error (MAE), respectively. In order to provide some context to these goodness of fit measures, we also considered the Interrupted Time Series Analysis (ITSA) model by using the R package its.analysis (English,...
Calibration curves, discrimination, mean squared error (MSE) and relative percentage of root MSE (RMSE%) were assessed by external validation of models in more-recent patients (n = 3768). Non-Bayesian fixed-effects GLMs were also applied and their outcomes were compared to these of the ...
VAF indicates the degree of correlation between the predicted results of the model and the actual results. The closer the value of VAF is to 100, the higher the prediction accuracy of the model. RMSE and MAE indicate the deviation between predicted results of the model and the actual results...
the latent feature learning effect and the recommendation effect. The ability of a model to predict latent factor vectors is an important part affecting recommendation performance. Therefore, we use Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Squa...
, root-mean-square error (RMSE), maximum absolute error (MAE), variance accounted for (VAF), and coefficient of determination (R[Math Processing Error]). While the error tends to rise for higher stiffness values, the correlation remains excellent, resulting in an R[Math Processing Error] ...
We apply CurcAGN to predicting the protein-ligand binding affinity. We train and validate our model on the publicly available standard PDBbind-v2016 dataset, and show that it outperforms SIGN [2] by 7.5% in RMSE and 9.4% in MAE.
On the other hand, the values of RMSE=10.000−1∑q=110.000(Yq+1(0)−Yq+1∖q∗(0))2 are highly related to the values of the dispersion measure. Therefore, we reach the finding that the aggregation of the predictions provides more accurate one-step-ahead predictions despite the ...
The form of information minimization in the IB is not necessary via the dimension reduction or the addition of noise, but it can be via any lossy operation, such as lossy compression or masking. We propose to address the IB principle for MTS via source masking, dimension reduction, and ...
The MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and PPCC (Probability Plot Correlation Coefficient) values were calculated using the goodness-of-fit test method, and the results are shown in Table 1. The optimal distribution of runoff from the Yellow River is a gamma ...