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      Question

      What is the 'bias-variance tradeoff' in machine

      learning?
      A The tradeoff between model training speed and prediction accuracy of the model Correct Answer Incorrect Answer
      B The tradeoff between model interpretability and prediction accuracy of the model Correct Answer Incorrect Answer
      C The tradeoff between the number of features used for training and the model's computational complexity Correct Answer Incorrect Answer
      D The tradeoff between a model's ability to fit training data well and its sensitivity to fluctuations in training data Correct Answer Incorrect Answer
      E The tradeoff between the size of training data and model performance improvement Correct Answer Incorrect Answer

      Solution

      Bias is the systematic error from incorrect assumptions. High bias models (linear regression on non-linear data) underfit. Variance means sensitivity to training data fluctuations. High variance models (deep decision trees) overfit.  Total Error = Bias² + Variance + Irreducible Noise.  As model complexity increases Bias decreases and Variance increases.  Optimal model complexity minimizes total error.  Solutions to this problem are Regularization (reduces variance), Ensemble methods (Bagging reduces variance, Boosting reduces bias) and More data (reduces variance). In banking, a credit model with high bias may systematically underestimate risk for a demographic group.

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