Bootstrap Sampling in Machine Learning

In machine learning, one of the most important tasks is to build models that can perform well on new, unseen data. To achieve this, we need reliable ways to estimate model performance.
Bootstrap Sampling is a simple yet powerful statistical technique that helps us understand how well a model will perform by creating multiple random samples from the original dataset.

How Bootstrap Sampling Works

Bootstrap Sampling is a method used to estimate the accuracy of machine learning models.
The idea is to repeatedly take random samples with replacement from the dataset and train or test the model on these samples.
Since sampling is done with replacement, some data points may appear multiple times in one sample, while others may not appear at all.

• Start with the original dataset.
• Randomly draw samples of the same size as the dataset, but with replacement.
• Build and evaluate the model on each sample.
• Repeat the process multiple times and combine the results for performance estimation.

This method allows us to approximate the distribution of a model’s performance and reduces the dependency on just one training-test split.

Bootstrap Sampling vs K-Fold Cross Validation

	Bootstrap Sampling	K-Fold Cross Validation
Sampling Method	Samples are drawn with replacement from the dataset.	Dataset is split into K distinct folds without replacement.
Sample Size	Each bootstrap sample is the same size as the original dataset.	Each training set is smaller than the full dataset (since one fold is left for testing).
Data Repetition	Some data points may appear multiple times in one sample, while others may be excluded.	Each data point is used exactly once in the test set and K-1 times in training sets.
Performance Estimate	Provides an approximation of the model’s performance distribution.	Provides an average performance score across all K folds.
Use Case	Good for small datasets and estimating model variability.	Commonly used for performance evaluation and model selection.