CodeBhavya

AI & MACHINE LEARNING PROGRAM • LEVEL 09 — TREES & FORESTS

Calculate Information Gain with Python

Learn calculate information gain with python with a short, executable Python example.

IntermediateInformation gainSplit

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
0.6

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-09-information-gain.py
Open in compiler
parent_entropy = 1.0
weighted_child_entropy = 0.4
print(parent_entropy - weighted_child_entropy)

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

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EXPECTED OUTPUT FOR THE SAMPLE

0.6
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Initialize the sample values used to calculate information gain.
  2. Apply Information gain and Split to compute the required result.
  3. Display the result for calculate information gain and compare it with the documented sample output.

This example of calculate information gain computes the result directly from the prepared sample data. It demonstrates Information gain and Split 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 information gain, keep the data shape and value types consistent with Information gain.

Check this

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

Check this

Verify the final Information gain and Split 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.