Model Fit and Performance Metrics
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Questions and Answers

What is one of the benefits of cross-validation in model evaluation?

  • It simplifies the model training process
  • It eliminates the need for hyperparameter tuning
  • It reduces computational resources required
  • It provides a more accurate estimate of model performance (correct)
  • What is the primary goal of overfitting detection in machine learning?

  • To detect if the model is overfitting to the training data (correct)
  • To reduce the computational resources required
  • To eliminate the need for hyperparameter tuning
  • To simplify the model training process
  • What is the purpose of adding a penalty term to the cost function in regularization?

  • To eliminate the need for hyperparameter tuning
  • To discourage the model from learning complex patterns (correct)
  • To increase the complexity of the model
  • To reduce the learning rate of the model
  • What is the concept of heteroscedasticity in regression modeling?

    <p>Varying variance of residuals across all levels of the independent variables</p> Signup and view all the answers

    What is the effect of heteroscedasticity on the assumptions of linear regression?

    <p>It violates the assumption of constant variance of residuals</p> Signup and view all the answers

    What is the primary benefit of cross-validation in hyperparameter tuning?

    <p>It provides a more comprehensive evaluation of the model's performance</p> Signup and view all the answers

    What is the difference between Lasso and Ridge regularization techniques?

    <p>Lasso uses L1 regularization, while Ridge uses L2 regularization</p> Signup and view all the answers

    Why is cross-validation important in machine learning?

    <p>It provides a more accurate estimate of model performance on unseen data</p> Signup and view all the answers

    What is the primary benefit of regularization in regression models?

    <p>It prevents overfitting by adding a penalty term to the cost function</p> Signup and view all the answers

    What is the effect of heteroscedasticity on the parameters of linear regression?

    <p>It leads to inefficient parameter estimates</p> Signup and view all the answers

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