🟪 1-Minute Summary
Overfitting occurs when a model learns the training data too well, including noise and outliers, performing poorly on new data. Signs: high training accuracy, low validation/test accuracy. Causes: model too complex, too little data, training too long. Solutions: regularization, more data, simpler model, cross-validation, early stopping, dropout (neural nets).
🟦 Core Notes (Must-Know)
What is Overfitting?
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How to Detect Overfitting
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Causes of Overfitting
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Solutions
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- Regularization (L1/L2)
- More training data
- Simpler model
- Cross-validation
- Early stopping
- Dropout (neural networks)
- Pruning (decision trees)
🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“What is overfitting?”
- [Answer: Model memorizes training data, poor generalization]
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“How do you detect overfitting?”
- [Answer: Large gap between train and validation performance]
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“What’s worse: overfitting or underfitting?”
- [Answer: Depends - both are bad, overfitting more common in practice]
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“How do you fix overfitting?”
- [Answer: List solutions above]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Only looking at training accuracy
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Mistake 2: Making model more complex to fix overfitting
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🟩 Mini Example (Quick Application)
Scenario
[Polynomial regression overfitting example]
Solution
# Example showing overfitting vs good fit
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