Evaluating the predictive power of machine learning model for shear transformation in metallic glasses using metrics for an imbalanced dataset

HIGHLIGHTS

• who: . et al. from the Oita University, Japan have published the paper: Evaluating the predictive power of machine learning model for shear transformation in metallic glasses using metrics for an imbalanced dataset, in the Journal: (JOURNAL)
• what: To prevent the model from being biased toward the major class during training due to the class imbalance, the weighted cross-entropy is used as a loss function.
• how: The test dataset was evaluated by the Frontiers in Materials frontiersin.org 10.3389/fmats.2022.874339 Optimized for displacement increases the accuracy of the trained model . . .
  

   

  

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