Supervised Learning CT-AI Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   Which of the following is a common supervised learning algorithm used for predicting a continuous numeric value?

   • ASupport vector machine
   • BLinear regression
   • CLogistic regression
   • DDecision tree
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2. 22application · medium

   A data scientist splits a dataset into training and test sets. The model performs well on the training set but poorly on the test set. Which of the following is the most likely cause?

   • AThe model is overfitting the training data
   • BThe test set is too similar to the training set
   • CThe training set is too large
   • DThe model is underfitting the training data
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4. 23expert · hard

   A data science team is building a model to predict patient readmission within 30 days. The dataset is highly imbalanced: only 5% of patients are readmitted. They train a model and achieve 95% accuracy on the test set, but the model predicts 'no readmission' for all patients. The team is considering using accuracy as the primary metric. What is the best course of action?

   • AUse precision, recall, or F1-score instead of accuracy
   • BUse accuracy because it is the standard metric
   • CUse the F1-score only if the model is a regression model
   • DUse the training accuracy to evaluate the model
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5. 24foundation · easy

   What is the key difference between classification and regression in supervised learning?

   • AClassification predicts discrete categories, while regression predicts continuous numeric values.
   • BClassification is used for prediction, while regression is used for clustering.
   • CClassification requires a test set, while regression does not.
   • DClassification uses labeled data, while regression uses unlabeled data.
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6. 25foundation · easy

   What is the role of labeled data in supervised learning?

   • ATo reduce the dimensionality of the input features before training.
   • BTo identify clusters of similar data points in the input space.
   • CTo provide the correct output for each input example, which the model uses to learn the mapping.
   • DTo provide a reward signal that guides the model's actions.
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