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What is the fundamental relationship between bias and variance when modifying a learning algorithm?
Question: Based on Andrew Ng's Machine Learning Yearning, describe the fundamental relationship or tradeoff that occurs when making changes to a learning algorithm's bias and variance.
Sample answer: The relationship is a tradeoff: changes that reduce bias errors generally do so at the cost of increasing variance, whereas changes that reduce variance generally do so at the cost of increasing bias.
Key points:
- Some changes to a learning algorithm reduce bias errors at the cost of increasing variance.
- Other changes to a learning algorithm reduce variance errors at the cost of increasing bias.
Rubric: The response must explain that a tradeoff exists where reducing bias increases variance, and reducing variance increases bias.
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What is the fundamental relationship between bias and variance when modifying a learning algorithm?