🟪 1-Minute Summary
MAPE expresses error as a percentage of actual values: (100/n) * Σ|actual - predicted|/|actual|. Useful when relative error matters more than absolute error. Pros: scale-independent, interpretable. Cons: undefined when actual = 0, asymmetric (over-predictions penalized less), biased toward low forecasts. Use for business metrics (sales, revenue) where % error is meaningful.
🟦 Core Notes (Must-Know)
Formula
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Interpretation
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When to Use MAPE
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Limitations
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“When would you use MAPE over RMSE?”
- [Answer: When relative error matters, comparing across scales]
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“What’s a major limitation of MAPE?”
- [Answer: Undefined when actual = 0, asymmetric]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using MAPE when data contains zeros
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🟩 Mini Example (Quick Application)
Scenario
[Sales forecast evaluation]
Solution
import numpy as np
def mape(actual, predicted):
return np.mean(np.abs((actual - predicted) / actual)) * 100
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