Awọn igi Ipinnu ati Idanwo Awọn igbo ID

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

1. Bawo ni igi ipinnu ṣe asọtẹlẹ?
   • Nipa aropin gbogbo awọn piksẹli ni aworan kan
   • Nipa titumọ ọrọ laarin awọn ede
   • Nipa lairotẹlẹ lafaimo ni akoko kọọkan
   • Nipa titẹle lẹsẹsẹ awọn ibeere bẹẹni/ko si isalẹ si ewe kan
2. Kini ailera akọkọ ti ọkan, igi ipinnu jinlẹ?
   • Ko le mu awọn nọmba mu
   • O duro lati overfit ati akosori ariwo ni data ikẹkọ
   • O le jade ọrọ nikan
   • O nilo GPU kan lati ṣiṣẹ
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. Metiriki wo ni awọn igi ipinya lo lati yan pipin?
   • Imọlẹ Pixel
   • Gini aimọ tabi entropy
   • Igbohunsafẹfẹ
   • Iwọn ọrọ
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