K-Nearest Neighbors Quiz

Answers and explanations appear as you take the quiz.

1. How does KNN classify a new data point?
   • By taking a majority vote of its K closest neighbors
   • By training a neural network on it
   • By computing a weighted regression line
   • By assigning a random class
2. Why is KNN often called a “lazy learner”?
   • It never stores training data
   • It stores training examples and can defer much model-specific work until prediction, though indexes may be built during fitting
   • It always trains a deep neural network first
   • It does no work during either fitting or prediction
3. Why is feature scaling important for KNN?
   • It makes training faster
   • It reduces the number of neighbors needed
   • So features with large ranges do not dominate the distance
   • It changes K automatically
4. What happens if you choose a very small K, like K=1?
   • The model becomes very smooth
   • The model ignores the data
   • Predictions become impossible
   • The model becomes sensitive to noise and may overfit
5. In a brute-force KNN implementation, why can prediction be slow on a large dataset?
   • It may compute distances from a query to many or all stored examples
   • It must update all model weights for every query
   • It reruns the entire training dataset through a neural network
   • It cannot use an index or approximate search
6. Which modern technology relies on the nearest-neighbor idea?
   • Image compression with JPEG
   • Vector search and retrieval-augmented generation
   • Random number generation
   • Spreadsheet formulas
7. Why should a feature scaler be fit only on the training split before KNN evaluation?
   • To make the distance metric ignore every feature
   • To guarantee all classes have equal counts
   • To avoid using information from validation or test examples when setting the scale
   • To remove the need to choose K
8. How can KNN make a prediction for a regression task?
   • Choose the most common class label only
   • Fit a new deep network for each query
   • Ignore the stored examples and use a fixed intercept
   • Aggregate the target values of the K nearest training examples, often by averaging