Microsoft Azure AI Fundamentals (AI-900) Practice Exam

Does a validation set verify that all training data was used to train the model?

1. Yes

2. No

3. Only in supervised learning

4. Depends on the machine learning algorithm

The correct answer is: No

A validation set serves a critical role in assessing the performance of a machine learning model, but it does not verify that all training data was used to train the model. Instead, the primary function of a validation set is to evaluate how well the model can generalize to unseen data. During the training process, the model learns from the training data, and the validation set allows for tuning the model's parameters and making adjustments to improve performance. When a model is trained, it utilizes the training dataset. The validation dataset, which is separate from the training dataset, is used to test the model's predictions and overall performance. This separation ensures that the evaluation of the model is done independently of the data it was trained on. If the validation data were part of the training data, it would lead to overfitting, where the model performs well on the training data but poorly on new, unseen data. Thus, while a validation set is an essential tool in the model evaluation process, it does not serve to confirm the usage of all training data. It specifically focuses on validating the model's performance based on a different set of data.