Kediska Deriska ugu dhow K-

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Sidee KNN u kala saartaa barta xogta cusub?
   • Iyaga oo qaata aqlabiyadda deriska ugu dhow ee K
   • Iyadoo lagu tababarayo shabakad neerfaha ah
   • Iyadoo la xisaabinayo khad dib u celin miisaan leh
   • Adiga oo u xilsaaray fasal random ah
2. Why is KNN often called a “lazy learner”?
   • It never stores training data
   • It stores training examples and can defer much model-specific work until prediction, though indexes may be built during fitting
   • It always trains a deep neural network first
   • It does no work during either fitting or prediction
3. Maxay tahay sababta cabirka astaamuhu muhiim ugu yahay KNN?
   • Waxay ka dhigtaa tababarka si dhakhso ah
   • Waxay yaraynaysaa tirada deriska loo baahan yahay
   • Sidaa darteed sifooyinka leh kala duwanaansho weyn kama badna masaafada
   • Si toos ah ayey u beddeshaa K
4. Maxaa dhacaya haddii aad doorato K aadka u yar, sida K=1?
   • Qaabku wuxuu noqonayaa mid aad u siman
   • Qaabka ayaa iska indha tiraya xogta
   • Saadaasha waxay noqotaa mid aan macquul ahayn
   • Qaabku wuxuu noqonayaa mid xasaasi u ah buuqa waxaana laga yaabaa inuu xad dhaaf yahay
5. In a brute-force KNN implementation, why can prediction be slow on a large dataset?
   • It may compute distances from a query to many or all stored examples
   • It must update all model weights for every query
   • It reruns the entire training dataset through a neural network
   • It cannot use an index or approximate search
6. Waa kuwee tignoolajiyada casriga ah ee ku tiirsan fikradda deriska- dhow?
   • Isku-buufinta sawirka leh JPEG
   • Raadinta iyo soo-celinta-jiilka la kordhiyey
   • Jiilka tirada random
   • Qaababka xaashida
7. Why should a feature scaler be fit only on the training split before KNN evaluation?
   • To make the distance metric ignore every feature
   • To guarantee all classes have equal counts
   • To avoid using information from validation or test examples when setting the scale
   • To remove the need to choose K
8. How can KNN make a prediction for a regression task?
   • Choose the most common class label only
   • Fit a new deep network for each query
   • Ignore the stored examples and use a fixed intercept
   • Aggregate the target values of the K nearest training examples, often by averaging