Supervised Learning GMLE Practice Questions (Page 5)

Questions 21–25

1. 21application · medium

   A team is developing a model to predict customer satisfaction scores. They have a dataset of 10,000 labeled records. To select the best model among several candidates, they use k-fold cross-validation. What is the main advantage of this approach over a single train/test split?

   • AIt eliminates the need for a separate test set
   • BIt increases the total amount of data available
   • CIt guarantees the model will not overfit
   • DIt uses the data more efficiently and provides a more stable performance estimate
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2. 22foundation · easy

   A model predicts whether an email is spam or not spam. What type of supervised learning task is this?

   • AClustering
   • BClassification
   • CRegression
   • DDimensionality reduction
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4. 23application · medium

   A retail company wants to predict the exact dollar amount a customer will spend in the next month based on their past purchase history, demographics, and browsing behavior. The data science team has a labeled dataset of historical customer transactions. Which model evaluation metric is most appropriate for this task?

   • AArea under the ROC curve (AUC)
   • BMean squared error (MSE)
   • CF1-score
   • DAccuracy
   Reveal Solution Discussion Select an answer first
5. 24expert · hard

   A data science team is developing a model to predict customer churn. They have a dataset with 20,000 labeled customers. They train a deep neural network and achieve 99% accuracy on the training set, but only 70% accuracy on the validation set. They also notice that the validation loss increases after a certain number of epochs. Which combination of actions is most effective to address this issue?

   • AUse a more complex model with more layers and neurons
   • BAdd dropout layers and early stopping based on validation loss
   • CIncrease the number of epochs and reduce the batch size
   • DRemove the validation set and train on all data to improve accuracy
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6. 25application · medium

   A model is trained to detect fraudulent transactions. The dataset has 1,000 fraudulent and 99,000 legitimate transactions. The model predicts 900 fraudulent transactions correctly but also flags 1,000 legitimate transactions as fraudulent. What is the F1-score for the fraudulent class?

   • A900 / 1000 = 0.9
   • B2 * (precision * recall) / (precision + recall) = 2 * (0.474 * 0.9) / (0.474 + 0.9) ≈ 0.621
   • C900 / 1900 = 0.474
   • D(900 + 99,000) / 100,000 = 0.999
   Reveal Solution Discussion Select an answer first