Kuis Pohon Keputusan dan Hutan Acak

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

1. Bagaimana pohon keputusan membuat prediksi?
   • Dengan merata-ratakan semua piksel dalam sebuah gambar
   • Dengan menerjemahkan teks antar bahasa
   • Dengan menebak secara acak setiap kali
   • Dengan mengikuti serangkaian pertanyaan ya/tidak sampai ke tahap awal
2. Apa kelemahan utama dari pohon keputusan tunggal yang mendalam?
   • Itu tidak bisa menangani angka
   • Ini cenderung melakukan overfit dan menghafal noise dalam data pelatihan
   • Itu hanya dapat menampilkan teks
   • Ini membutuhkan GPU untuk menjalankannya
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. Metrik apa yang biasa digunakan pohon klasifikasi untuk memilih pemisahan?
   • Kecerahan piksel
   • Gini pengotor atau entropi
   • Frekuensi audio
   • Jumlah kata
6. Which named system is described in the paper A Scalable Tree Boosting System?
   • photoshop
   • FFmpeg
   • Kubernet
   • 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