Kediska Modeling Model

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. In the LDA formulation, how is a modeled topic represented over the vocabulary?
   • As one mandatory human-written keyword only
   • As a probability distribution over words
   • As a grammatical parse of every sentence
   • As a sentiment value with no vocabulary representation
2. Why can an unsupervised LDA topic model be fit to documents without topic labels?
   • It requires a verified category for every document first
   • It reads only documents with identical titles
   • It necessarily uses a classifier trained on the same task labels
   • It infers unobserved topic structure from the observed words
3. Sidee LDA u daweysaa dukumeenti gaar ah?
   • Sida ka tirsan hal mawduuc oo sax ah
   • Sida isku xigxiga ee habaynta kelmadu lagama maarmaan u tahay
   • Sida isku dhafka mawduucyo badan oo kala duwan
   • Sida sawir qoraal ah oo la iskaan karo
4. Waa maxay malo-awaalka 'bac-of-era' ee ay isticmaasho LDA-ga caadiga ah?
   • Waxay iska indha tirtaa habaynta kelmad waxayna adeegsataa tirinta kelmadda
   • Waxa kaliya oo ay ilaalisaa jumlada koowaad ee dukumeenti kasta
   • Waxay marka hore u tarjumeysaa dukumeenti kasta Ingiriisi
   • Waxay u baahan tahay ereyada inay u kala horreeyaan alifbeetada
5. Go'aankee ayay tahay inaad sida caadiga ah samayso ka hor inta aanad bilaabin LDA?
   • Cinwaanka saxda ah ee mawduuc kasta
   • Qoraaga dukumeenti kasta
   • Farta dukumentiyada ayaa lagu qoray
   • Tirada mawduucyada K si aad u hesho
6. Which stage commonly starts BERTopic's grouping of documents rather than relying only on raw word counts?
   • Assigning every document a manually required topic label
   • Clustering representations derived from document embeddings
   • Removing all text before computing any representation
   • Treating every document as an identical image file
7. When inspecting LDA's high-probability words, who can supply a descriptive topic name such as election coverage?
   • An analyst interpreting the weighted words and documents
   • A probability value that necessarily proves one official name
   • A required government approver for every model run
   • A document filename that always determines the meaning
8. After BERTopic groups documents, what does c-TF-IDF help produce for each cluster?
   • The original input image pixels
   • A required dependency parse for every token
   • Characteristic weighted words representing the group
   • Proof that every cluster has exactly one true theme