K-Tumo si iṣupọ adanwo

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

1. Kini 'K' ni K-Means tọka si?
   • Nọmba awọn iṣupọ ti o yan ni ilosiwaju
   • Awọn nọmba ti data ojuami
   • Awọn nọmba ti ikẹkọ iterations
   • Nọmba awọn ẹya ti o wa ninu dataset
2. Kini awọn igbesẹ atunwi meji ni lupu K-Means?
   • Pin awọn iṣupọ, lẹhinna dapọ awọn iṣupọ
   • Fi awọn aaye si centroid ti o sunmọ, lẹhinna gbe awọn centroids si itumọ
   • Aami data, ki o si irin a classifier
   • Too data, lẹhinna yọ awọn ita kuro
3. Iwọn wo ni K-Means gbiyanju lati dinku?
   • Nọmba awọn iṣupọ
   • Aaye laarin centroids
   • Laarin iṣupọ ijinna onigun mẹrin (inertia)
   • Nọmba awọn ẹya ara ẹrọ
4. Kini idi ti a npe ni K-Means ohun algoridimu 'abojuto'?
   • It uses predefined class labels to learn a boundary
   • It requires one label per initial centroid
   • It estimates labels only after supervised training
   • It groups inputs without target class labels
5. Kini 'ọna igbonwo' ti a nlo nigbagbogbo fun?
   • Iyara ikẹkọ
   • Yiyan iye to dara ti K
   • Bibẹrẹ awọn centroids
   • Yiyọ àdáwòkọ ojuami
6. Kini idi ti K-Means le fun awọn abajade oriṣiriṣi lori awọn ṣiṣe oriṣiriṣi?
   • K automatically changes on every run
   • Euclidean distance uses a random definition
   • Results depend on initial centroid positions
   • Convergence always produces the same global optimum
7. A K-Means cluster contains the one-dimensional values 2, 4, and 9. Where does its centroid move in the update step?
   • 5, the mean of the assigned values
   • 4, the middle observed value
   • 9, the largest assigned value
   • 15, the sum of the assigned values
8. A dataset contains two long, curved groups. Which K-Means limitation should the analyst consider?
   • The groups must have labels before clustering
   • K-Means always discovers curved boundaries exactly
   • Changing K guarantees the intended grouping
   • Euclidean distance to centroids can poorly represent elongated or non-spherical groups