Maswali yanayounganishwa na mwongozo · Rahisi Kiwango

Maswali kuhusu Miti ya Uamuzi na Misitu Nasibu

Kagua njia za maamuzi ya miti, uwekaji kupita kiasi, ujumlishaji, sampuli za bootstrap, vigezo vya mgawanyiko, nyongeza, vipengele vya nasibu, na tathmini ya nje ya mfuko.

Njia za mwongozo zinazohusianaMiti ya Uamuzi na Misitu ya Nasibu
Swali 1 ya 8

Je, mti wa maamuzi hufanyaje utabiri?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. Je, mti wa maamuzi hufanyaje utabiri?
    • Kwa wastani wa saizi zote kwenye picha
    • Kwa kutafsiri maandishi kati ya lugha
    • Kwa kubahatisha nasibu kila wakati
    • Kwa kufuata msururu wa maswali ya ndiyo/hapana hadi kwenye jani
  2. Ni udhaifu gani kuu wa mti mmoja wa uamuzi wa kina?
    • Haiwezi kushughulikia nambari
    • Inaelekea kupita kiasi na kukariri kelele katika data ya mafunzo
    • Inaweza kutoa maandishi pekee
    • Inahitaji GPU kuendesha
  3. How does a random forest combine the predictions of its constituent trees?
    • By aggregating predictions, such as class votes or probabilities
    • By using exactly one tree and discarding every other output
    • By converting all trees into one fixed training row
    • By guaranteeing that all individual errors cancel
  4. What sampling operation does bagging use when constructing conventional bootstrap training sets?
    • Sorting rows only by filename
    • Permanently deleting all numerical features
    • Drawing training rows with replacement
    • Assigning exactly one unique row to every tree
  5. Je, miti ya uainishaji hutumia metriki gani kuchagua mgawanyiko?
    • Mwangaza wa pikseli
    • Uchafu wa Gini au entropy
    • Mzunguko wa sauti
    • Idadi ya maneno
  6. Which named system is described in the paper A Scalable Tree Boosting System?
    • Photoshop
    • FFmpeg
    • Kubernetes
    • XGBoost
  7. Which random-forest mechanism varies candidate input features at a split rather than resampling training rows?
    • Bootstrap row sampling alone
    • Alphabetical label sorting
    • Random feature-subset selection
    • A guarantee of identical splits in every tree
  8. For a training row in a bootstrap forest, which trees should contribute to its out-of-bag prediction?
    • Trees whose bootstrap samples omitted that row
    • Only trees whose samples included that row at least twice
    • Every tree regardless of whether it trained on the row
    • A required newly trained tree using only that row