Ensemble Nzira uye Gradient Boosting Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. What is the core operation in an ensemble method?
   • Combining predictions from multiple models
   • Kushandisa modhi imwe hombe kwazvo pane madiki akawanda
   • Kudzidzira modhi pane yakawanda data sezvinobvira
   • Kubvisa vadzidzi vese vasina simba kubva pamuenzaniso
2. How does gradient boosting differ from bagging in the usual training setup?
   • Kusimudzira kunodzidzisa miti yakafanana; bagging inovadzidzisa zvakatevedzana
   • Boosting adds learners sequentially; independently fitted bagging learners can be trained in parallel
   • Kukurudzira kunoshanda chete pamifananidzo; kubhegi pamavara chete
   • Iwo akafanana mukuita
3. In gradient boosting, what target does a new tree approximate?
   • Iwo manyorero epakutanga
   • A random subset of the features chete
   • The negative gradient of the current loss, often called a pseudo-residual
   • Avhareji yemiti yose yapfuura
4. What is a possible effect of using a smaller learning rate in boosting?
   • Kuwedzera saizi yemuti wega wega
   • Kudzima miti isingaite zvakanaka
   • Kushandura miti kuita neural network
   • 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
   • High-resolution mifananidzo
   • Raw audio waveforms
   • Mapepa marefu emutauro wechisikigo
6. Ndeipi nzira yakajairika yekugadzika nzira inoshandiswa kuramba ichiwedzera kubva pakuwandisa?
   • Kubvisa basa rekurasikirwa
   • Kudzikamisa kudzika kwemuti uye subsampling mitsara / maficha
   • Kushandisa chiyero chekudzidza che 1.0 nguva dzose
   • Kudzidzira miti yakawanda isingaperi
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