Research Article
Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions
Table 2
The performance comparison of models applying various sampling techniques to the RF [
9].
| | Without sampling | Oversampling | Undersampling | Hybrid sampling | SMOTE | ADASYN | TL | ENN | OSS | CNN | Smote+ENN | Smote+TL |
| Accuracy | Training set | 1.0 | 0.985 | 0.973 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.987 | 5-fold cross-validation | | | | | | | | | | Test set | 0.85 | 0.9 | 0.85 | 0.8 | 0.65 | 0.6 | 0.5 | 0.9 | 0.9 | Precision | Training set | 1.0 | 0.985 | 0.975 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.987 | 5-fold cross-validation | | | | | | | | | | Test set | 0.83 | 0.933 | 0.83 | 0.77 | 0.842 | 0.739 | 0.647 | 0.933 | 0.933 | Recall | Training set | 1.0 | 0.985 | 0.973 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.987 | 5-fold cross-validation | | | | | | | | | | Test set | 0.85 | 0.9 | 0.85 | 0.8 | 0.65 | 0.6 | 0.5 | 0.9 | 0.9 | F1-score | Training set | 1.0 | 0.985 | 0.973 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.987 | 5-fold cross-validation | | | | | | | | | | Test set | 0.832 | 0.906 | 0.832 | 0.783 | 0.688 | 0.607 | 0.559 | 0.906 | 0.906 |
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