Isolation Forest Quiz

Answers and explanations appear as you take the quiz.

1. What tends to happen to an unusual point's average isolation-tree path length?
   • It is shorter because fewer random splits isolate it
   • It is longer because every split keeps it with common points
   • It equals the number of features exactly
   • It is set by contamination directly
2. What does the contamination setting commonly control?
   • The verified prevalence of anomalies in production
   • The threshold used to label a chosen fraction as outliers
   • The number of input features
   • The random split direction for every tree
3. Which score interpretation matches the forest's core intuition?
   • Longer paths always mean higher anomaly risk
   • A score is a calibrated fraud probability
   • Shorter paths indicate stronger isolation-based anomaly evidence
   • The score identifies the causal reason for an event
4. A rare but legitimate customer segment receives high anomaly scores. What issue does this illustrate?
   • Path lengths are always causal
   • Contamination guarantees correct labels
   • Random trees require class labels
   • Rarity is not equivalent to fraud or invalidity
5. Why verify score direction in the chosen library?
   • All libraries define negative scores as normal
   • Some decision functions invert or shift the raw anomaly score
   • The tree ensemble omits path lengths
   • Contamination reverses the feature matrix
6. If contamination increases from 5% to 10%, what change is most directly expected?
   • Every path becomes shorter
   • The true anomaly rate is proven to double
   • More points may be labeled anomalous by the threshold
   • The model changes into supervised fraud classification
7. How should an alerting team evaluate an Isolation Forest threshold when labels are available?
   • Choose the threshold that labels exactly half the data
   • Assume every short path is fraud
   • Compare only the training score average
   • Measure precision and recall tradeoffs on representative labeled cases
8. What can an Isolation Forest score not provide by itself?
   • A causal explanation or calibrated event probability
   • A ranking of isolation behavior
   • An ensemble-based anomaly score
   • A comparison across observations