Logistic Regression DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   What is the primary purpose of logistic regression in predictive modeling?

   • ATo reduce the dimensionality of a dataset while preserving variance
   • BTo group similar observations into clusters based on feature similarity
   • CTo model the probability that an observation belongs to one of two categories
   • DTo predict a continuous numeric outcome based on one or more predictor variables
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2. 22application · medium

   A bank wants to predict whether a loan applicant will default (yes/no) based on credit score, income, and loan amount. The data has 10,000 records with a 2% default rate. Which modeling approach is most appropriate as a starting point?

   • ALinear regression predicting a continuous default probability
   • BLogistic regression with a binary outcome and appropriate handling of class imbalance
   • CA decision tree because it handles non-linear relationships better
   • DLogistic regression without any consideration of the low default rate
   Reveal Solution Discussion Select an answer first
4. 23expert · hard

   A data science team builds a logistic regression model to predict customer churn. The model has high accuracy (95%) but very low recall (20%). The business wants to identify as many churners as possible for retention offers, even if it means contacting some non-churners. Which action is most appropriate?

   • ARetrain the model with more features to improve overall performance
   • BUse F1-score as the optimization metric and tune the threshold to maximize it
   • CLower the classification threshold to increase recall, accepting lower precision
   • DRaise the classification threshold to increase precision
   Reveal Solution Discussion Select an answer first
5. 24foundation · easy

   What method is used to estimate the coefficients in a logistic regression model?

   • AMaximum likelihood estimation (MLE)
   • BOrdinary least squares (OLS)
   • CPrincipal component analysis (PCA)
   • DGradient descent only
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6. 25foundation · easy

   What does the likelihood function represent in the context of logistic regression?

   • AThe probability of observing the sample data given the model parameters
   • BThe probability of the model parameters given the observed data
   • CThe sum of squared errors between predicted and actual outcomes
   • DThe accuracy of the model on the training data
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