🟪 1-Minute Summary
R² (coefficient of determination) measures the proportion of variance in the target explained by the model. Range: -∞ to 1 (1 = perfect fit, 0 = model is no better than mean, negative = worse than mean). Formula: 1 - (SS_residual / SS_total). Adjusted R² penalizes adding useless features. Use for model comparison, but not as the only metric.
🟦 Core Notes (Must-Know)
What is R²?
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Formula
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Interpretation
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Adjusted R²
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Limitations
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“What does R² = 0.8 mean?”
- [Answer: 80% of variance in target is explained by the model]
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“Can R² be negative?”
- [Answer: Yes - model is worse than predicting the mean]
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“Why use adjusted R² instead of R²?”
- [Answer: Penalizes adding features that don’t improve fit]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using R² as the only evaluation metric
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Mistake 2: Thinking higher R² always means better model
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🟩 Mini Example (Quick Application)
Scenario
[Compare models using R²]
Solution
from sklearn.metrics import r2_score
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