Model Selection and Evaluation MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   A machine learning engineer must model a continuous target variable that is expected to have a non-linear relationship with several numeric predictors. The dataset contains 1 million rows and 20 columns. Which algorithm is the most appropriate initial choice?

   • ALinear Regression
   • BK-Nearest Neighbors (KNN)
   • CGradient Boosted Trees (GBT)
   • DSupport Vector Regression (SVR) with RBF kernel
   Reveal Solution Discussion Select an answer first
2. 2foundation · easy

   A model has high training accuracy but significantly lower test accuracy. According to the bias-variance tradeoff, what is the most likely issue?

   • AHigh variance (overfitting)
   • BThe model is perfectly balanced
   • CBoth high bias and high variance
   • DHigh bias (underfitting)
   Reveal Solution Discussion Select an answer first
4. 3application · medium

   A healthcare startup is building a model to screen for a rare disease. The model's output is a probability score. The team needs to compare two models: Model A has an AUC of 0.85, and Model B has an AUC of 0.82. However, Model A's precision-recall curve shows a steep drop in precision at high recall, while Model B maintains a more stable precision-recall trade-off. The business requirement is to maximize the number of true positives while keeping false positives below 5%. Which model should they choose and why?

   • AModel A, because higher AUC always indicates a better model for imbalanced datasets.
   • BModel A, because AUC is the standard metric for model comparison in all cases.
   • CModel B, because lower AUC means the model is simpler and less prone to overfitting.
   • DModel B, because its precision-recall curve indicates a better trade-off at the required operating point.
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   A data scientist trains a linear regression model with a log-transformed target variable. The model predicts a value of 2.302585 on the log scale. What is the predicted value on the original scale?

   • A2.302585
   • B5.302585
   • C0.1
   • D10
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6. 5foundation · easy

   A data scientist is comparing two regression models. Model A has an RMSE of 10 and an MAE of 8. Model B has an RMSE of 12 and an MAE of 7. Which statement is correct?

   • AModel A has a lower RMSE, but Model B has a lower MAE.
   • BThe models are equivalent because the metrics are close.
   • CModel B is better on both metrics.
   • DModel A is better on both metrics.
   Reveal Solution Discussion Select an answer first