Kedis ku xidhan hagaha · Dhexdhexaad Heerka
Dhibcaha Silhouette iyo Kediska Qiimaynta Kooxda
Xisaabi qiyamka silhouette-dhibic oo kala saar baadhisyada joomatari ee gudaha ka faa'iidaysiga kooxda iyo tusmooyinka kale.
Su'aal 1 ee 8
Markay wataan a=2 iyo b=5, waa maxay isugeynta silhouette (b-a)/max(a,b)?
Horudhac su'aal kasta8 su'aalo
Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.
- Markay wataan a=2 iyo b=5, waa maxay isugeynta silhouette (b-a)/max(a,b)?
- 0.6
- -0.6
- 0.4
- 1.5
- Dhibicdu waxay leedahay a=4 iyo b=3. Maxay tilmaamaysaa silhouette xun?
- Its assigned cluster has zero observations
- It is, on average, closer to another cluster under the chosen distance
- The global score must be negative
- The feature scaling is proven correct
- Maxay silhouette u dhow eber caadi ahaan u soo jeedinaysaa hal fiirsi?
- Its class label is known
- It is perfectly separated from all clusters
- It lies near a boundary between its own and another cluster
- The clustering has one cluster only
- Jihohee ayaa guud ahaan doorbidaya tusmada Davies-Bouldin?
- A value matching the silhouette range
- Higher values
- Exactly zero for every useful partition
- Lower values
- Waa maxay sababta qayriinka Davies-Bouldin iyo Calinski-Harabasz aan si toos ah loo barbar dhigin?
- Both are probability estimates between zero and one
- They use different formulas, scales and preferred directions
- They require identical units by definition
- One is always the reciprocal of the other
- Why can an average silhouette hide a cluster-specific problem?
- Every sample receives a different formula
- Silhouette uses only labels and no distances
- A sample-weighted mean can mask weak values in a smaller group
- A negative value is omitted automatically
- What does a high silhouette score fail to establish by itself?
- That labels were assigned
- That within and between distances were calculated
- That the chosen metric was used
- That the clusters are useful for the downstream task
- When is the silhouette score undefined in common implementations?
- For degenerate labelings with one cluster or one cluster per sample
- Whenever the average coefficient is positive
- When a cluster has two observations
- When distances use standardized features