AI & MACHINE LEARNING PROGRAM • LEVEL 11 — ENSEMBLE LEARNING

Create a Bootstrap Sample with Python

Learn create a bootstrap sample with python with a short, executable Python example.

IntermediateBaggingBootstrap

PROBLEM UNDERSTANDING

Input and expected output

Sample input

No input required

Sample output

[1, 3, 2, 3]

COMPLETE PYTHON PROGRAM

Complete Python implementation

import random
random.seed(3)
data = [1,2,3,4]
print(random.choices(data, k=len(data)))

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

No line selected

EXPECTED OUTPUT FOR THE SAMPLE

[1, 3, 2, 3]

0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

Import the required standard-library tools and prepare the sample data for create a bootstrap sample.
Apply Bagging and Bootstrap to compute the required result.
Display the result for create a bootstrap sample and compare it with the documented sample output.

This example of create a bootstrap sample computes the result directly from the prepared sample data. It demonstrates Bagging and Bootstrap and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(n)

Auxiliary space

O(n)

DEBUGGING CHECKLIST

Common mistakes

Check this

For create a bootstrap sample, keep the data shape and value types consistent with Bagging.

Check this

Apply Bagging in the same order shown by the algorithm; changing the order can change the result.

Check this

Verify the final Bagging and Bootstrap result against the sample before trying new data.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.