Maswali yanayounganishwa na mwongozo · Kati Kiwango

Kusanya Mbinu na Maswali ya Kukuza Gradient

Linganisha kuweka mifuko na kuongeza kasi, kusawazisha mabaki, kupungua, kuratibu, matumizi ya data ya jedwali, na zana za kutathmini kwa vikundi vya miti.

Njia za mwongozo zinazohusianaNjia za Kukusanya na Kuongeza Gradient
Swali 1 ya 8

What is the core operation in an ensemble method?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. What is the core operation in an ensemble method?
    • Combining predictions from multiple models
    • Kwa kutumia modeli moja kubwa sana badala ya nyingi ndogo
    • Kufundisha mfano juu ya data nyingi iwezekanavyo
    • Kuondoa wanafunzi wote dhaifu kutoka kwa mfano
  2. How does gradient boosting differ from bagging in the usual training setup?
    • Kuongeza treni miti sambamba; bagging huwafunza kwa kufuatana
    • Boosting adds learners sequentially; independently fitted bagging learners can be trained in parallel
    • Kuongeza kazi tu kwenye picha; kuweka kwenye maandishi tu
    • Wanafanana katika mazoezi
  3. In gradient boosting, what target does a new tree approximate?
    • Lebo mbichi asili
    • Sehemu ndogo ya nasibu ya vipengele pekee
    • The negative gradient of the current loss, often called a pseudo-residual
    • Wastani wa miti yote iliyopita
  4. What is a possible effect of using a smaller learning rate in boosting?
    • Ili kuongeza ukubwa wa kila mti
    • Ili kufuta miti ambayo hufanya kazi vibaya
    • Kubadilisha miti kuwa mitandao ya neva
    • It can make updates more gradual and may require more trees
  5. What type of input is used in the cited gradient-boosted-tree examples and studies?
    • Structured or tabular data
    • Picha za azimio la juu
    • Mawimbi ghafi ya sauti
    • Hati ndefu za lugha asilia
  6. Ni mbinu ipi ya kawaida ya urekebishaji inayotumika kuzuia kuongezeka kutoka kwa kuzidisha?
    • Kuondoa kazi ya kupoteza
    • Kuweka kikomo kina cha mti na safu mlalo/vipengele vidogo vya sampuli
    • Kutumia kiwango cha kujifunza cha 1.0 kila wakati
    • Kufundisha miti mingi isiyo na kikomo
  7. What does an out-of-bag score estimate in a bagged tree ensemble?
    • The accuracy on examples used by every tree
    • The number of leaves in the largest tree
    • Generalization performance using training examples omitted from a tree’s bootstrap sample
    • The learning rate of a gradient-boosted model
  8. What does early stopping monitor during gradient-boosting training?
    • Only the training-set row order
    • Whether the model has no trees left
    • The number of categorical columns in a spreadsheet
    • A validation metric and stops adding trees when it no longer improves