Table 4 The effect of various inner-loop learning rate \(\boldsymbol{\alpha }\) on performance of MetaILMC
From: Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors
| Â | Inner-loop learning rate \(\alpha\) | ||||
|---|---|---|---|---|---|
| Â | 0.1 | 0.05 | 0.01 | 0.001 | 0.0001 |
AUC | 0.8518 | 0.859 | 0.8754 | 0.8365 | 0.8304 |
AUPR | 0.6945 | 0.702 | 0.7265 | 0.6599 | 0.6465 |