Hello Nagwa,
It seems like you're asking whether not using validation error for a few short learning models would lead to a larger generalization error.
The validation error is crucial for assessing the model's performance during training and tuning hyperparameters. The decision to split the data when working with limited data and using short learning models depends on the specific model you are using. In general, having a validation dataset helps prevent overfitting in the model. However, if you already have a small dataset, overfitting might not be a significant concern, so not splitting the data into a validation dataset could be acceptable.
Nevertheless, it is recommended to follow best practices, such as trying cross-validation and different scenarios, to determine what works best for your dataset and the specific few short learning algorithm you are using.
Hope it helps,
Krishna.
