IBM Data Science Practice Test

What action should be taken to mitigate the risk of overfitting?

1. Do not use hold out data to select models.

2. Only use training data.

3. Collect much more data.

4. Utilize hold out data to evaluate model performance on new data.

The correct answer is: Utilize hold out data to evaluate model performance on new data.

Mitigating the risk of overfitting involves ensuring that a model generalizes well to unseen data rather than merely memorizing the training data. Utilizing hold-out data is essential for this purpose. This approach entails setting aside a portion of the data that the model has not seen during training. By evaluating the model's performance on this hold-out dataset, you can gain insights into how well the model is likely to perform in real-world scenarios. This method helps to identify if the model is overfitting to the training data, as a model that is overfitting will typically show high accuracy on training data but poor accuracy on hold-out data. Therefore, by assessing performance on separate validation or test datasets, you can make informed decisions about model selection and potential adjustments, such as regularization or feature selection, which further combat overfitting. The other options do not contribute to managing overfitting effectively. For example, not using hold-out data prevents any evaluation of how the model will perform on new, unseen data. Relying solely on training data ignores the validation necessary to assess model generalization. Simply collecting more data can help but is not a definitive solution to overfitting; the quality of the model and its ability to generalize is