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Data Science
Evaluation
A/B Testing
Statistical comparison of model variants to determine superior performance.
Intent & Description
📋 Context
Comparing model versions requires rigorous statistical testing. A/B testing exposes different models to user segments and measures outcomes.
Real-world Use Case
Comparing model variants in production to determine which performs better on business metrics.
Source
Advantages
- Statistical rigor
- Real-world validation
- Business alignment
- Incremental rollout
Disadvantages
- Long duration
- Complex setup
- Statistical power requirements
- Ethical considerations
Implementation Example
# A/B Testing Pattern import scipy.stats as stats
def ab_test(conversion_a, conversion_b): # Perform t-test to compare conversion rates t_stat, p_value = stats.ttest_ind(conversion_a, conversion_b)
if p_value < 0.05: return "Statistically significant difference" else: return "No significant difference"
# Example usage model_a_conversions = [1, 0, 1, 1, 0, 1, 1, 0, 1, 1] model_b_conversions = [1, 1, 1, 1, 1, 0, 1, 1, 1, 1]
result = ab_test(model_a_conversions, model_b_conversions)