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Prueba de aprendizaje no supervisado

Explore la agrupación, la escala de características, la reducción de dimensiones, las comprobaciones de estabilidad y cómo las puntuaciones de anomalías respaldan la investigación sin probar una causa.

Rutas de guía relacionadasAprendizaje no supervisado
Pregunta 1 de 8

A dataset has no labels, but a team wants to group similar records. Which family of methods fits that goal?

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Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

  1. A dataset has no labels, but a team wants to group similar records. Which family of methods fits that goal?
    • Aprendizaje no supervisado
    • Clasificación supervisada
    • Target encoding
    • Regresión
  2. Two customer records differ by one visit and 1,000 dollars of spending. What can raw Euclidean distance do?
    • Automatically treat the features equally
    • Be dominated by the spending difference because feature scales differ
    • Prove that the records belong to different real categories
    • Remove the need to inspect features
  3. A two-dimensional projection shows two separated clouds. What limitation should an analyst remember?
    • Every distance in the plot exactly preserves all original distances
    • The projection proves the groups are true categories
    • The projection discards information, so apparent gaps are not unquestionable facts about the original data
    • Visualization removes the need to inspect raw dimensions
  4. A clustering result changes after reasonable preprocessing choices. What should the team do?
    • Treat preprocessing as irrelevant
    • Name the first result as a discovered truth
    • Choose whichever plot looks most separated
    • Examine stability and inspect representative and borderline records
  5. A cluster is built from purchase frequency rather than product preferences. What follows?
    • The cluster names are fixed by the algorithm
    • The similarity rule and features affect what groupings appear
    • Both analyses must return identical segments
    • Purchase frequency proves customer intent
  6. An internal clustering score is high. What does that establish?
    • That the cluster will remain stable in all settings
    • That the group is a true human category
    • A mathematical property under that score, not automatic usefulness for the real purpose
    • That anomalies are harmful
  7. An anomaly detector flags an unusual transaction. What can the alert support?
    • Investigation, but not proof of misconduct
    • An automatic finding of fraud
    • A diagnosis of the account owner
    • A guaranteed reason for the unusual value
  8. A team scales features and sees different neighbors. Why is scaling relevant?
    • It makes high-dimensional data lossless
    • It supplies target labels
    • It proves a better category system
    • It changes how feature differences contribute to similarity