Awọn ọna akojọpọ ati Idanwo Igbelaruge Gradient

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Kini iṣẹ mojuto ni ọna akojọpọ kan?
   • Combining predictions from multiple models
   • Lilo awoṣe ti o tobi pupọ dipo ọpọlọpọ awọn kekere
   • Ikẹkọ awoṣe lori data pupọ bi o ti ṣee
   • Yiyọ gbogbo awọn akẹẹkọ alailagbara lati awoṣe kan
2. Bawo ni igbega gradient ṣe yato si apo ni iṣeto ikẹkọ deede?
   • Igbelaruge reluwe igi ni afiwe; bagging reluwe wọn lesese
   • Boosting adds learners sequentially; independently fitted bagging learners can be trained in parallel
   • Igbelaruge ṣiṣẹ nikan lori awọn aworan; bagging nikan lori ọrọ
   • Wọn jẹ aami kanna ni iṣe
3. Ni igbega gradient, ibi-afẹde wo ni igi tuntun ṣe isunmọ?
   • Awọn aami aise atilẹba
   • Ipin aileto ti awọn ẹya nikan
   • The negative gradient of the current loss, often called a pseudo-residual
   • Awọn apapọ ti gbogbo awọn ti tẹlẹ igi
4. Kini ipa ti o ṣee ṣe ti lilo iwọn ẹkọ ti o kere ju ni igbega?
   • Lati mu iwọn igi kọọkan pọ si
   • Lati pa awọn igi ti ko ṣiṣẹ daradara
   • Lati yi awọn igi pada si awọn nẹtiwọọki nkankikan
   • It can make updates more gradual and may require more trees
5. Iru igbewọle wo ni a lo ninu awọn apẹẹrẹ igi-giradient-igbelaruge ti a tọka ati awọn ikẹkọ?
   • Structured or tabular data
   • Awọn aworan ti o ga
   • Aise iwe igbi
   • Awọn iwe aṣẹ-ede adayeba gigun
6. Ewo ni ilana isọdọtun ti o wọpọ ti a lo lati tọju igbelaruge lati fifẹ?
   • Yiyọ iṣẹ isonu kuro
   • Idiwọn ijinle igi ati awọn ori ila-apapọ / awọn ẹya ara ẹrọ
   • Lilo oṣuwọn ikẹkọ ti deede 1.0 nigbagbogbo
   • Ikẹkọ ailopin ọpọlọpọ awọn igi
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