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Why is resampling used in statistical learning?
Why is resampling used in statistical learning?
- To underestimate the test error rate
- To fit a model of interest to samples formed from the training subset (correct)
- To directly obtain a large designated test set
- To separate a dataset into training and testing subsets
What is the main purpose of cross-validation in statistical learning?
What is the main purpose of cross-validation in statistical learning?
- To divide the available set of samples into two parts
- To estimate test error from the resulting validation set error (correct)
- To quantify the uncertainty associated with a given estimator
- To identify the method that results in the highest test error
In the context of Bootstrap, what does 'bootstrap data sets' refer to?
In the context of Bootstrap, what does 'bootstrap data sets' refer to?
- Data sets obtained by fitting a model of interest to samples formed from the training subset
- Data sets used to directly estimate the prediction error
- Data sets created by sampling with replacement from the original data set (correct)
- Data sets created by dividing the available samples into training and validation sets
What is the aim of fitting a model to samples formed from a training subset?
What is the aim of fitting a model to samples formed from a training subset?
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What is the main purpose of using Bootstrap in statistical learning?
What is the main purpose of using Bootstrap in statistical learning?
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How does Cross Validation help in statistical learning?
How does Cross Validation help in statistical learning?
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