Sarudzo Miti uye Random Masango Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Muti wesarudzo unoita sei kufanotaura?
   • Nekuenzanisa mapikseli ese mumufananidzo
   • Nekushandura mavara pakati pemitauro
   • Nekungofungidzira nguva imwe neimwe
   • Nekutevera nhevedzano yemibvunzo hongu/kwete kusvika pashizha
2. Ndeipi kushaya simba kukuru kwemuti mumwe chete, wakadzika wesarudzo?
   • Haikwanise kubata nhamba
   • Inowanzoita kuwandisa uye nemusoro ruzha mune data rekudzidzisa
   • Inogona chete kubuda zvinyorwa
   • Zvinoda GPU kumhanya
3. How does a random forest combine the predictions of its constituent trees?
   • By aggregating predictions, such as class votes or probabilities
   • By using exactly one tree and discarding every other output
   • By converting all trees into one fixed training row
   • By guaranteeing that all individual errors cancel
4. What sampling operation does bagging use when constructing conventional bootstrap training sets?
   • Sorting rows only by filename
   • Permanently deleting all numerical features
   • Drawing training rows with replacement
   • Assigning exactly one unique row to every tree
5. Ndeapi metric anowanzo shandisa miti yekuisa pakusarudza kupatsanura?
   • Pixel kupenya
   • Gini tsvina kana entropy
   • Audio frequency
   • Shoko kuverenga
6. Which named system is described in the paper A Scalable Tree Boosting System?
   • Photoshop
   • FFmpeg
   • Kubernetes
   • XGBoost
7. Which random-forest mechanism varies candidate input features at a split rather than resampling training rows?
   • Bootstrap row sampling alone
   • Alphabetical label sorting
   • Random feature-subset selection
   • A guarantee of identical splits in every tree
8. For a training row in a bootstrap forest, which trees should contribute to its out-of-bag prediction?
   • Trees whose bootstrap samples omitted that row
   • Only trees whose samples included that row at least twice
   • Every tree regardless of whether it trained on the row
   • A required newly trained tree using only that row