Supervised Learning GMLE Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A bank wants to predict whether a loan applicant will default (yes/no) based on credit history, income, and employment status. The model must output a probability of default so the bank can set different interest rates. Which algorithm is most appropriate?

   • ALogistic regression
   • BApriori algorithm
   • CK-means clustering
   • DLinear regression
   Reveal Solution Discussion Select an answer first
2. 2foundation · easy

   Which evaluation metric is most appropriate for a regression model predicting house prices?

   • AF1-score
   • BPrecision
   • CAccuracy
   • DMean squared error (MSE)
   Reveal Solution Discussion Select an answer first
4. 3expert · hard

   A team is training a logistic regression model for a binary classification task. They notice that the model performs well on the training set but poorly on the validation set. They suspect overfitting. Which combination of actions is most likely to reduce overfitting while maintaining model interpretability?

   • AApply L1 regularization and use cross-validation to select the regularization strength
   • BAdd polynomial features to increase model capacity
   • CRemove the validation set and train on all data
   • DIncrease the number of training epochs
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   A data scientist is building a model that learns from historical customer records where each record includes a label indicating whether the customer churned. The model will use these labeled examples to predict churn for new customers. Which type of machine learning is this?

   • AUnsupervised learning
   • BSupervised learning
   • CReinforcement learning
   • DSemi-supervised learning
   Reveal Solution Discussion Select an answer first
6. 5expert · hard

   A data scientist is training a regularized logistic regression model. The training loss is low, but validation loss is high. They increase the regularization strength (C decreases). After retraining, both training and validation loss are high. What is the most likely issue?

   • AThe test set is too small
   • BThe learning rate is too high
   • CThe model is still overfitting because regularization is not effective
   • DThe regularization strength is now too high, causing underfitting
   Reveal Solution Discussion Select an answer first