IBM Data Science Practice Test

In context to machine learning models, what does hold out sample help with?

1. Increasing the size of the training set.

2. Evaluating the model's performance on unseen data.

3. Only for testing purposes.

4. Creating new training samples.

The correct answer is: Evaluating the model's performance on unseen data.

The hold-out sample plays a crucial role in assessing how well a machine learning model performs on data that it has not encountered during training. By setting aside a portion of the dataset as a hold-out sample, you allow the model to be evaluated on this unseen data, which provides a more accurate gauge of its predictive capabilities and generalization to real-world scenarios. This process helps identify potential problems like overfitting, where the model may perform exceptionally well on the training data but poorly on new data. While it might seem that the hold-out sample could also be used for testing purposes exclusively, its primary function is intertwined with evaluating model performance, allowing for a clearer understanding of how the model would perform in practice when deployed. Hence, the hold-out sample is not merely for testing; instead, it is a vital component in the model validation process. The other options suggest roles for the hold-out sample that do not align with its established purpose. Increasing the training set size or creating new training samples are not functions of a hold-out sample, as it is meant to maintain a separation between training and evaluation. Therefore, evaluating the model's performance on unseen data is the primary and correct purpose of the hold-out sample.