What is bootstrapping in deep learning?

What is bootstrapping in deep learning?

Bootstrapping in deep learning refers to a statistical method used to estimate the distribution of a sample by resampling with replacement. It helps in improving model performance and assessing the stability of predictions.

How does bootstrapping improve model performance?

Bootstrapping allows for the creation of multiple training datasets from a single dataset, which can help in reducing overfitting and improving the model's generalization by providing diverse training examples.

Can you give an example of bootstrapping in practice?

Sure! In practice, bootstrapping can be used in ensemble methods like bagging, where multiple models are trained on different bootstrapped samples of the data to create a more robust final model.

What are the limitations of bootstrapping?

Some limitations include the potential for overfitting if the original dataset is small, and it may not capture the true variability of the population if the sample is not representative.