Key performance metrics for regression models include the mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), determination coefficient (R-squared), and mean absolute percentage error (MAPE). The average importance of errors with a given array of forecasts is ...
What is more, some facets of the performance of a classification system reveal with precision, recall and their weighted harmonic mean, namely f-measure. If someone wants to know that, what proportion of system’s true labeled predictions (TP + FP), has actual true labels (TP), precision ...
(2020) built machine learning models for predicting maize yield in the US corn belt and achieved well-performing models with 9% mean absolute percentage error. Other studies have focused on estimating crop yield at higher spatial resolution, e.g. by incorporating 10-m resolution data from ...
Sinha and Wang used RMSD (Root-mean-square-deviation), RMSF (Root-mean-square-fluctuations), Rg (Radius of gyration), SASA (Solvent accessible surface area), NH bond (hydrogen bond) and covariance analysis calculated from molecular dynamics simulations to predict whether or not unclassified ...
The F-score (Equation (3)) is the harmonic mean of the precision and recall metrics. For the loss metric, binary cross-entropy loss has been employed. This loss function is applicable for not only binary classification tasks but also multilabel classification tasks. It evaluates the ...
The elevation of the watershed generally ranges between 1782 and 3712 m above mean sea level (amsl) (Figure 1c). The watershed is characterized by a mountainous terrain with a steep gradient in the upstream part and an undulating terrain with a gentle gradient in the downstream part of the ...