AI & MACHINE LEARNING PROGRAM • LEVEL 25 — MLOPS & RESPONSIBLE AI
Compare Selection Rates with Python
Learn compare selection rates with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
{'A': 0.8, 'B': 0.6} 0.20000000000000007COMPLETE PYTHON PROGRAM
Complete Python implementation
selected={'A':8,'B':6}
total={'A':10,'B':10}
rates={group:selected[group]/total[group] for group in selected}
print(rates,abs(rates['A']-rates['B']))CURRENT STEP
Select Start to walk through the important lines.
SELECTED LINE
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EXPECTED OUTPUT FOR THE SAMPLE
{'A': 0.8, 'B': 0.6} 0.20000000000000007PROGRAM EXPLANATION
Algorithm and explanation
- Initialize the sample values used to compare selection rates.
- Process the data step by step using Fairness and Demographic parity.
- Display the result for compare selection rates and compare it with the documented sample output.
This example of compare selection rates processes the sample values in a controlled iteration. It demonstrates Fairness and Demographic parity and prints a deterministic result that can be checked against the sample output.
EFFICIENCY
Time and space complexity
O(n)
O(1)
DEBUGGING CHECKLIST
Common mistakes
For compare selection rates, keep the data shape and value types consistent with Fairness.
Keep every dependent statement inside the correct indented Python block.
Check denominators, numeric ranges and rounding before comparing the calculated value.