10-601 Bias-Variance

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What you should know

  • How overfitting/underfitting can be understood as a tradeoff between high-bias and high-variance learners.
  • Mathematically, how to decompose error for linear regression into bias and variance.
  • Intuitively, how classification can be decomposed into bias and variance.
  • Which sorts of classifier variants lead to more bias and/or more variance: e.g., large vs small k in k-NN, etc.