Xeetu ensemble ak Quiz buy yokk degrade

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. What is the core operation in an ensemble method?
   • Combining predictions from multiple models
   • Jëfandikoo benn model bu rëy lool ci palaasu model yu ndaw yu bari
   • Taggat benn model ci done yu bari
   • Dindi bépp jàngat bu néew doole ci benn misaal
2. How does gradient boosting differ from bagging in the usual training setup?
   • Yokkateg saxaar garab yi ci paralel; sac di leen tàggat ñu toppalante
   • Boosting adds learners sequentially; independently fitted bagging learners can be trained in parallel
   • Boosting ci nataal yi kese lay dox; sac ci mbind kese
   • Dañuy nuru ci jëf
3. In gradient boosting, what target does a new tree approximate?
   • Etiket yu ñor yi
   • ab ensemble bu bari ci man-man yi kese
   • The negative gradient of the current loss, often called a pseudo-residual
   • Moyenne garab yi njëkk yépp
4. What is a possible effect of using a smaller learning rate in boosting?
   • Yokk dayo garab gu nekk
   • Ngir efaase garab yu baaxul
   • Soppi garab yi def ko reso neuronal
   • 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
   • Nataal yu am dayo bu kawe
   • Formu onde audio yu ñor
   • Këyitu làkk wiñ nàmp
6. Lan mooy pexem regularisasioŋ biñ gëna jëfandikoo ngir moytu boosting bi ëpp loxo?
   • Dindi fonction perte
   • Yamale xóotaayu garab gi ak jël misaal ci ligne/man-man yi
   • Jëfandikool tolluwaayu jàng bu tollu ci 1.0 saa yu nekk
   • Taggat garab yu bari yu amul àpp
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