Regressions GMLE Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   What does an R-squared value of 0.85 indicate in a regression model?

   • AThe model's predictions are 85% accurate in terms of correct classification
   • BThe model has an average error of 0.85 units
   • C85% of the data points lie exactly on the regression line
   • D85% of the variance in the dependent variable is explained by the independent variables
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2. 7application · medium

   A data scientist is evaluating a regression model that predicts delivery times. The model has an MAE of 2 minutes and an RMSE of 5 minutes. What does the difference between MAE and RMSE indicate?

   • AThe model has some predictions with large errors, since RMSE is higher than MAE.
   • BThe model has a bias problem.
   • CThe model has no errors, since both are positive.
   • DThe model is overfitting the training data.
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4. 8expert · medium

   A data scientist is building a linear regression model. The residual plot shows a clear curved pattern. The model's R-squared is 0.80. What should the data scientist do?

   • AApply a log transformation to the target variable.
   • BRemove outliers from the dataset.
   • CAdd polynomial terms to the model.
   • DUse ridge regression to reduce overfitting.
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5. 9foundation · easy

   In logistic regression, how is the decision boundary typically determined?

   • ABy finding the point where the sigmoid function equals zero
   • BBy maximizing the R-squared value of the model
   • CBy setting a threshold on the predicted probability, commonly 0.5
   • DBy setting the coefficients to zero
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6. 10expert · medium

   A data scientist is training a logistic regression model. The training accuracy is 95%, but the validation accuracy is 70%. The dataset is balanced. What is the most likely cause and the best next step?

   • AThe model is fine; accuracy differences are normal.
   • BThe validation set is too small; use a larger validation set.
   • CThe model is underfitting; add more features or increase model complexity.
   • DThe model is overfitting; use regularization like L2 or reduce model complexity.
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