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Why can reducing regularization help a high-bias model?
Question: Answer in one to three sentences: Why might reducing or eliminating L1, L2, or dropout regularization help when avoidable bias is high, and what is the tradeoff?
Sample answer: Reducing or eliminating regularization can lower avoidable bias. The tradeoff is that variance increases.
Key points:
- Regularization can be reduced or eliminated.
- Avoidable bias decreases.
- Variance increases.
Rubric: The answer must state both effects: reduced avoidable bias and increased variance.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
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Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
Modifying Input Features Based on Error Analysis to Reduce Avoidable Bias
Reducing or Eliminating Regularization to Reduce Avoidable Bias
Which intervention directly targets high avoidable bias by improving training-set fit?
Adding more training data usually has no significant effect on avoidable bias.
Complete the remedy: Reduce or eliminate _____ to lower avoidable bias.
Match each intervention with its stated effect or rationale.
Order the reasoning process for responding to poor training-set performance.
Explain how the four recommended techniques address high avoidable bias.
Choose remedies for a model that performs poorly on its training set.
Why can reducing regularization help a high-bias model?
Which response best uses error analysis to reduce avoidable bias?
Changing model architecture may influence both bias and variance.