Which of the following statements about bootstrapping is true?<br /> A. 1. Bootstrapping can be applied to construct prediction intervals revealing uncertainty in predictions. <br />B. 2. Bootstrapping randomly selects samples with replacement from the original dataset. <br />C. 3. Bootstrapping involves creating multiple smaller datasets from the original dataset. <br />D. 4. Bootstrapping requires dividing the dataset into multiple folds for training and testing.
Question
Which of the following statements about bootstrapping is true?<br /> A. 1. Bootstrapping can be applied to construct prediction intervals revealing uncertainty in predictions. <br />B. 2. Bootstrapping randomly selects samples with replacement from the original dataset. <br />C. 3. Bootstrapping involves creating multiple smaller datasets from the original dataset. <br />D. 4. Bootstrapping requires dividing the dataset into multiple folds for training and testing.
Solution
The correct answer is A, B, and C.
A. Bootstrapping can indeed be used to construct prediction intervals, which reveal uncertainty in predictions. This is done by generating multiple resamples of the original data and then making predictions on each resample. The variability of these predictions can then be used to estimate the prediction interval.
B. Bootstrapping does involve randomly selecting samples with replacement from the original dataset. This means that the same data point can be selected more than once in a single resample.
C. While it's not necessarily about creating "smaller" datasets, bootstrapping does involve creating multiple datasets from the original one. Each of these datasets is a resample of the original data.
D. This statement is not true. Bootstrapping does not require dividing the dataset into multiple folds for training and testing. This is a description of cross-validation, not bootstrapping.
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