AI & MACHINE LEARNING PROGRAM • LEVEL 08 — KNN & NAIVE BAYES
Compare Naive Bayes Scores with Python
Learn compare naive bayes scores with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
spam {'spam': 0.32000000000000006, 'ham': 0.12}COMPLETE PYTHON PROGRAM
Complete Python implementation
priors = {'spam':0.4, 'ham':0.6}
likelihood = {'spam':0.8, 'ham':0.2}
scores = {label: priors[label]*likelihood[label] for label in priors}
print(max(scores, key=scores.get), scores)CURRENT STEP
Select Start to walk through the important lines.
SELECTED LINE
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EXPECTED OUTPUT FOR THE SAMPLE
spam {'spam': 0.32000000000000006, 'ham': 0.12}PROGRAM EXPLANATION
Algorithm and explanation
- Initialize the sample values used to compare naive bayes scores.
- Process the data step by step using Naive Bayes and Posterior score.
- Display the result for compare naive bayes scores and compare it with the documented sample output.
This example of compare naive bayes scores processes the sample values in a controlled iteration. It demonstrates Naive Bayes and Posterior score 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 naive bayes scores, keep the data shape and value types consistent with Naive Bayes.
Keep every dependent statement inside the correct indented Python block.
Check denominators, numeric ranges and rounding before comparing the calculated value.