In this study, meta-learning serves as a method to validate the proposed task hardness metric. For each test task, we define a level of task hardness. Additionally, we observe the extent to which the performance (e.g., ROC-AUC or AUPRC) improves through meta-learning. Subsequently, we compare these two metrics to determine whether an inverse correlation exists between task hardness and performance improvement from meta-learning. We anticipate that tasks with higher hardness values will benefit less from meta-learning.