Bootstrap Sampling: An Introduction to Python

Bootstrap Sampling is an important method in statistics that is also frequently applied in data analysis and machine learning. In this tutorial, we will look at what Bootstrap Sampling is and how it can be implemented in Python.

What is Bootstrap Sampling?

Bootstrap Sampling is a method in statistics where samples are drawn with replacement from a data source to estimate a population-related parameter. Instead of looking at the entire population, several subsets of equal size from the population are considered.

For example, instead of considering all 1000 entries of a population, we can take 50 samples of size 4 and calculate the mean for each sample. In this way, we are averaging 200 entries (50×4) that were randomly selected.

How is Bootstrap Sampling Implemented in Python?

To implement Bootstrap Sampling in Python, we use the NumPy and Random libraries. First, we import the necessary modules:

 
import numpy as np
import random

Then we generate some random data with a predetermined mean. In this example, we create a normal distribution with a mean of 300 and 1000 entries:

 
x = np.random.normal(loc=300.0, size=1000)

We can calculate the mean of this data:

 
print(np.mean(x))

Now we use Bootstrap Sampling to estimate the mean. We create 50 samples of size 4 and calculate the mean for each sample:

 
sample_mean = []

for i in range(50):
    y = random.sample(x.tolist(), 4)
    avg = np.mean(y)
    sample_mean.append(avg)

print(np.mean(sample_mean))

Each time we run this code, we get a different output, but it will always be close to the actual mean. This is the essence of Bootstrap Sampling—by drawing samples, we can estimate the population-related parameter without looking at the entire population.

Conclusion

Bootstrap Sampling is a powerful method for estimating population-related parameters by drawing samples. In this tutorial, we have seen how to implement Bootstrap Sampling in Python. This technique is particularly useful in the world of machine learning to avoid overfitting. We hope you enjoyed learning with us!