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      Question

      'Overfitting' in machine learning

      means:
      A a model is too simple and fails to capture the underlying pattern in data Correct Answer Incorrect Answer
      B a model uses too many computational resources Correct Answer Incorrect Answer
      C a model takes too long to train due to large dataset size Correct Answer Incorrect Answer
      D a model's predictions consistently deviate from true values in one direction Correct Answer Incorrect Answer
      E a model performs excellently on training data but poorly on unseen test data Correct Answer Incorrect Answer

      Solution

      Overfitting means high variance and low bias. The model memorizes training data rather than learning generalizable patterns. Overfitting occurs when training accuracy >> test accuracy. It can be prevented by Regularization (L1/Lasso adds |weights|, L2/Ridge adds weights² to loss function, penalizing complexity), Cross-validation (k-fold — evaluates on multiple train/test splits), Early stopping (stop training when validation loss increases), Dropout (neural networks — randomly deactivate neurons during training), Pruning (decision trees — remove low-information branches), More training data.  Underfitting means high bias and low variance resulting in model being too simple.

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