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NEW QUESTION: 1

A. Option C
B. Option D
C. Option A
D. Option B
Answer: C

NEW QUESTION: 2
소매 업체에서 기계 학습을 사용하여 신제품을 분류하려고 합니다. 현재 제품의 레이블이 지정된 데이터 세트가 데이터 과학 팀에 제공되었습니다. 데이터 세트에 1,200 개의 제품이 포함되어 있습니다.
제목 크기, 무게 및 가격과 같은 각 제품에 대한 15 가지 기능 각 제품에는 서적, 게임, 전자 제품 및 영화와 같은 6 가지 범주 중 하나에 속하는 것으로 표시되어 있습니다.
교육을 위해 제공된 데이터 세트를 사용하여 신제품을 분류하기 위해 어떤 모델을 사용해야 합니까?
A. 트리 수가 제품 범주 수와 동일하게 설정된 회귀 포리스트
B. 마지막 계층에 대해 softmax 활성화 기능이 있는 심층 컨볼 루션 신경망 (CNN)
C. RNN (Recurrent Neural Network) 기반의 DeepAR 예측 모델
D. objective 매개 변수가 multi로 설정된 XGBoost 모델 : softmax
Answer: D
Explanation:
A XGBoost multi class classification.
https://medium.com/@gabrielziegler3/multiclass-multilabel-classification-with-xgboost-
66195e4d9f2d
CNN is used for image classificaiton problems.

NEW QUESTION: 3
In biometric identification systems, at the beginning, it was soon apparent that truly positive identification could only be based on :
A. age of a person
B. sex of a person
C. voice of a person
D. physical attributes of a person
Answer: D
Explanation:
Explanation/Reference:
Today implementation of fast, accurate reliable and user-acceptable biometric identification systems is already under way.
From: TIPTON, Harold F. & KRAUSE, MICKI, Information Security Management Handbook, 4th Edition, Volume 1, Page 7.