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docs: finalized wiki integrity maintenance (v3.0 standard) - pruned 1400+ stubs and fixed 11k+ ghost links

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Antigravity Agent
2026-05-02 09:18:34 +09:00
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10_Wiki/Topics_Blog/Bias vs Variance.md
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@@ -6,7 +6,7 @@ tags: [auto-reinforced, bias-variance, machine-learning-foundations, overfitting
last_reinforced: 2026-04-20
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# [[Bias vs Variance]]
# [[Bias vs Variance|Bias vs Variance]]
## 📌 한 줄 통찰 (The Karpathy Summary)
> "학습의 영원한 줄다리기: 너무 단순해서 진실을 놓치는 '편향(Bias)'의 함정과, 너무 예민해서 훈련 데이터의 사소한 소음까지 다 믿어버리는 '분산(Variance)'의 역설 사이에서 황금 균형(Sweet Spot)을 찾는 과정."
@@ -27,6 +27,6 @@ last_reinforced: 2026-04-20
- **정책 변화(RL Update)**: 보상 함수 설계 정책에서, 모델의 분산을 줄이기 위해 데이터 증강(Augmentation)이나 규제화(Regularization)를 강제하는 '안정성 지향적 학습 정책'이 필수적으로 적용됨.
## 🔗 지식 연결 (Graph)
- [[Standardization vs Innovation]], [[stochastic gradient descent]], Foundational Models, Pattern Recognition, [[Stability vs Flexibility]]
- [[Standardization vs Innovation|Standardization vs Innovation]], [[stochastic gradient descent|stochastic gradient descent]], Foundational Models, Pattern Recognition, [[Stability vs Flexibility|Stability vs Flexibility]]
- **Modern Tech/Tools**: Cross-validation, Early stopping, Dropout, L1/L2 Regularization.
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