Kuiz Pokok Keputusan dan Hutan Rawak

Jawapan dan penjelasan muncul semasa anda mengambil kuiz.

1. Bagaimanakah pokok keputusan membuat ramalan?
   • Dengan purata semua piksel dalam imej
   • Dengan menterjemah teks antara bahasa
   • Dengan meneka secara rawak setiap kali
   • Dengan mengikuti satu siri soalan ya/tidak sehingga sehelai daun
2. Apakah kelemahan utama pokok keputusan tunggal yang mendalam?
   • Ia tidak boleh mengendalikan nombor
   • Ia cenderung untuk melebihkan dan menghafal bunyi dalam data latihan
   • Ia hanya boleh mengeluarkan teks
   • Ia memerlukan GPU untuk dijalankan
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. Apakah metrik yang biasa digunakan oleh pokok pengelasan untuk memilih pembahagian?
   • Kecerahan piksel
   • Kekotoran gini atau entropi
   • Frekuensi audio
   • Bilangan perkataan
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