Explain the bias–variance trade-off
Company: Amazon
Role: Applied Scientist
Category: Machine Learning
Difficulty: easy
Interview Round: Technical Screen
Explain the **bias–variance trade-off** in supervised learning.
In your answer, cover:
- What **bias** and **variance** mean in the context of a prediction model.
- How total expected error can be decomposed into bias, variance, and irreducible noise.
- How model complexity affects bias and variance (underfitting vs. overfitting).
- How you would use this concept in practice when choosing or tuning models.
Overview: This question evaluates understanding of the bias–variance trade-off in supervised learning, covering definitions of bias and variance, error decomposition into bias, variance, and irreducible noise, and the relation between model complexity, underfitting, and overfitting, and it belongs to the Machine Learning domain.
