Logistic Regression Quiz

Answers and explanations appear as you take the quiz.

1. Despite its name, what is logistic regression actually used for?
   • Classification into categories
   • Predicting continuous numbers
   • Clustering unlabeled data
   • Reducing dimensions
2. What does the sigmoid (logistic) function do in the model?
   • It picks the number of features
   • It maps any value to a probability between 0 and 1
   • It removes outliers
   • It clusters the data
3. Which loss function does logistic regression minimize during training?
   • Mean squared error
   • Inertia
   • Log loss (cross-entropy)
   • Hinge loss
4. Why is logistic regression valued for interpretability?
   • It uses no math
   • It hides its internal logic
   • It always predicts 50 percent
   • Each weight shows how a feature shifts the log-odds of the outcome
5. How does logistic regression typically turn a probability into a class label?
   • It applies a threshold, often 0.5
   • It always picks the first class
   • It averages neighbors
   • It uses random selection
6. How does logistic regression connect to neural networks?
   • It is unrelated to them
   • A neural net's final sigmoid or softmax layer is essentially logistic regression
   • It replaces all hidden layers
   • It can only run on GPUs
7. What does convexity tell us about the binary logistic-regression negative log-likelihood?
   • It guarantees exactly one finite solution for every dataset
   • It makes the model immune to mislabeled examples
   • A finite local minimum is global, but convexity alone does not guarantee uniqueness
   • It means the decision boundary must be nonlinear
8. What can complete separation imply for unregularized logistic-regression maximum likelihood?
   • The model must misclassify every training example
   • The sigmoid stops mapping scores to probabilities
   • The optimizer automatically adds regularization
   • There may be no finite maximum-likelihood estimate because coefficients can grow without bound