AI & MACHINE LEARNING PROGRAM • LEVEL 16 — RECOMMENDATION SYSTEMS
Calculate Precision at K with Python
Learn calculate precision at k with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
0.6666666666666666
COMPLETE PYTHON PROGRAM
Complete Python implementation
recommended=['A','B','C']
relevant={'A','C','D'}
print(sum(item in relevant for item in recommended)/len(recommended))CURRENT STEP
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SELECTED LINE
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EXPECTED OUTPUT FOR THE SAMPLE
0.6666666666666666
PROGRAM EXPLANATION
Algorithm and explanation
- Initialize the sample values used to calculate precision at k.
- Process the data step by step using Precision@K and Ranking metric.
- Display the result for calculate precision at k and compare it with the documented sample output.
This example of calculate precision at k processes the sample values in a controlled iteration. It demonstrates Precision@K and Ranking metric and prints a deterministic result that can be checked against the sample output.
EFFICIENCY
Time and space complexity
O(k)
O(1)
DEBUGGING CHECKLIST
Common mistakes
For calculate precision at k, keep the data shape and value types consistent with Precision@K.
Keep every dependent statement inside the correct indented Python block.
Check denominators, numeric ranges and rounding before comparing the calculated value.