Local Outlier Factor Quiz

Answers and explanations appear as you take the quiz.

1. What does an LOF score substantially above one suggest?
   • The point is less dense than its local neighbors
   • The point is equally dense as its local neighbors
   • It is far from the global center even if its neighborhood is dense
   • The score is a calibrated anomaly probability
2. Why can a far-away point receive an ordinary LOF score?
   • LOF ignores all neighbor information
   • It may belong to a separate dense subgroup with similar local density
   • Distance is always set to zero outside the main cloud
   • LOF measures only the global mean
3. What does increasing the neighbor count generally change?
   • The number of features used in the density ratio
   • The sign convention of the returned score
   • The scale over which local density is compared
   • The global clustering method used before LOF
4. Why does feature scaling matter for LOF?
   • It changes local density into a Gaussian parameter
   • It guarantees every feature contributes equally without preprocessing
   • It changes neighbor count into a class label
   • Nearest-neighbor distances depend on feature units
5. How does novelty detection differ from fitted-sample outlier detection?
   • It requires labels for every training sample
   • It scores new observations against a reference fit
   • It retrains a new density for each query
   • It reports only global cluster means
6. In scikit-learn novelty mode, what should users avoid according to its documentation?
   • Checking neighbor count
   • Scaling numeric predictors
   • Using novelty scoring methods on the training set
   • Scoring new unseen cases
7. Which pattern indicates a local rather than global anomaly concept?
   • Distance from the global center is large even when local density is high
   • The observation has a rare category label
   • The global cluster contains few observations
   • Density is low relative to nearby points
8. Why check the sign convention of an LOF API score?
   • Libraries may invert scores so stronger outliers have lower numeric values
   • The sign determines how many neighbors exist
   • LOF scores are always probabilities
   • The score direction is identical in every implementation