Cuestionario vinculado a una guía · Medio Nivel

Prueba del bosque de aislamiento

Conecte longitudes aleatorias de rutas de árboles con clasificaciones de anomalías y distinga un umbral de contaminación de una tasa de anomalías del mundo real.

Rutas de guía relacionadasBosque de aislamiento
Pregunta 1 de 8

What tends to happen to an unusual point's average isolation-tree path length?

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  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