🟪 1-Minute Summary
Regression metrics measure how well your model predicts continuous values. Common metrics: RMSE (penalizes large errors, same units), MAE (average absolute error, robust to outliers), MAPE (percentage error), R² (variance explained, 0-1). Choose based on context: RMSE for penalizing large errors, MAE for balanced view, MAPE for relative error, R² for overall fit.
🟦 Core Notes (Must-Know)
Common Regression Metrics
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When to Use Each Metric
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Interpreting Metrics
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Which regression metric would you use and why?”
- [Answer framework: Depends on context - explain tradeoffs]
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“What’s the difference between RMSE and MAE?”
- [Answer: RMSE penalizes large errors more]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using only R² to evaluate
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🟩 Mini Example (Quick Application)
Scenario
[Comparing metrics on same model]
Solution
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import numpy as np
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