🟪 1-Minute Summary
Underfitting occurs when a model is too simple to capture underlying patterns in data. Both training and validation performance are poor. Causes: model too simple, insufficient features, over-regularization. Solutions: more complex model, add features, reduce regularization, train longer. Less common than overfitting but equally problematic.
🟦 Core Notes (Must-Know)
What is Underfitting?
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How to Detect Underfitting
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Causes of Underfitting
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Solutions
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- Use more complex model
- Add more features
- Reduce regularization
- Train longer
- Feature engineering
🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“What is underfitting?”
- [Answer: Model too simple to capture patterns, poor on train AND test]
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“How do you distinguish underfitting from overfitting?”
- [Answer: Underfitting = both train/test bad, Overfitting = train good/test bad]
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“How do you fix underfitting?”
- [Answer: More complex model, more features, less regularization]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Always adding complexity
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Mistake 2: Confusing underfitting with overfitting
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🟩 Mini Example (Quick Application)
Scenario
[Linear model for non-linear data]
Solution
# Example showing underfitting
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🔗 Related Topics
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