Addressing Overfitting in Machine Learning
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Questions and Answers

What is one consequence of fitting a hypothesis of high degree to a set of points?

  • Overfitting (correct)
  • Optimal fitting
  • Underfitting
  • Converging
  • What is the main concern with overfitting in machine learning models?

  • Optimal prediction accuracy
  • High bias
  • Poor performance on unseen data (correct)
  • Low variance
  • How can overfitting be reduced in machine learning models?

  • By reducing the number of features (correct)
  • By ignoring the training data
  • By memorizing all training data points
  • By increasing the complexity of the model
  • Which technique helps prevent overfitting by adding a penalty for complex models?

    <p>Regularization</p> Signup and view all the answers

    What happens to prediction accuracy when a model overfits the training data?

    <p>High accuracy on training data, low accuracy on unseen data</p> Signup and view all the answers

    In machine learning, why is it important for a model to generalize well?

    <p>To perform consistently on new, unseen data</p> Signup and view all the answers

    What effect does high variance in a prediction curve have on a machine learning model's performance?

    <p>Increases overfitting</p> Signup and view all the answers

    How does underfitting differ from overfitting in machine learning models?

    <p>Underfitting has high bias, overfitting has high variance.</p> Signup and view all the answers

    What role does model complexity play in overfitting?

    <p>Simplifying model complexity helps prevent overfitting.</p> Signup and view all the answers

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