What is the Difference Between Bootstrapping and Cross Validation?
Bootstrapping and cross validation are two resampling techniques used in machine learning to evaluate the performance of a model. While both methods involve repeatedly dividing a dataset into smaller sets, they differ in how they do this and how they use the resulting sets.
Bootstrapping:
- Randomly samples a new dataset with replacement from the original dataset.
- The sampling process is repeated multiple times to create a set of new datasets called "bootstrapped samples."
- The model is trained and evaluated on each bootstrapped sample.
- The distribution of the model's performance across the bootstrapped samples provides an estimate of the model's accuracy and variability.
Cross Validation:
- Randomly divides the original dataset into k equal-sized subsets (folds).
- The model is trained on k-1 folds and evaluated on the remaining fold.
- This process is repeated k times, each time using a different fold for evaluation.
- The average performance of the model across the k folds provides an estimate of the model's accuracy and variability.
Key Differences:
- Sampling: Bootstrapping uses replacement, while cross validation does not.
- Evaluation: Bootstrapping evaluates the model on multiple randomly generated samples, while cross validation evaluates the model on different subsets of the original data.
- Accuracy Estimation: Bootstrapping provides an estimate of the variance of the model's performance, while cross validation provides an estimate of the model's bias.
Related Questions:
- What is the advantage of bootstrapping over cross validation? Bootstrapping allows for more efficient estimation of the model's variance.
- When is cross validation preferred over bootstrapping? Cross validation is preferred when the dataset is small or when it is important to avoid overfitting.
- Which resampling technique is more robust to outliers? Bootstrapping is more robust to outliers since it uses replacement during sampling.
- How many bootstrapped samples are typically used? The number of bootstrapped samples depends on the size of the dataset and the computational resources available.
- What are some alternative resampling techniques? Other resampling techniques include bagging, random forests, and stacking.
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