CodeBhavya

AI & MACHINE LEARNING PROGRAM • LEVEL 14 — DIMENSIONALITY REDUCTION

Calculate Explained Variance Ratio with Python

Learn calculate explained variance ratio with python with a short, executable Python example.

IntermediateExplained varianceEigenvalue

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
[0.6, 0.3, 0.1]

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-14-explained-variance.py
Open in compiler
eigenvalues = [6,3,1]
print([value/sum(eigenvalues) for value in eigenvalues])

GUIDED CODE TOUR • NOT LIVE EXECUTION

Study the program line by line

Use the real compiler button above to run and debug with different inputs.

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

No line selected

EXPECTED OUTPUT FOR THE SAMPLE

[0.6, 0.3, 0.1]
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Initialize the sample values used to calculate explained variance ratio.
  2. Process the data step by step using Explained variance and Eigenvalue.
  3. Display the result for calculate explained variance ratio and compare it with the documented sample output.

This example of calculate explained variance ratio processes the sample values in a controlled iteration. It demonstrates Explained variance and Eigenvalue 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(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For calculate explained variance ratio, keep the data shape and value types consistent with Explained variance.

Check this

Keep every dependent statement inside the correct indented Python block.

Check this

Check denominators, numeric ranges and rounding before comparing the calculated value.

Try it yourself

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