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NEW QUESTION: 1
提供されているトレーニングセットを使用して、バイナリ分類モデルを構築しています。
トレーニングセットは2つのクラス間で不均衡です。
データの不均衡を解決する必要があります。
この目標を達成するための3つの可能な方法は何ですか？各正解は完全なソリューションを示します注：
それぞれの正しい選択には1ポイントの価値があります。
A. マイノリティクラスで合成サンプルを生成します。
B. アンダーサンプリングまたはオーバーサンプリングを使用してデータセットをリサンプリングします
C. 分類にペナルティを科す
D. モデルの評価指標として精度を使用します。
E. トレーニング機能セットを正規化します。
Answer: B,C,D
Explanation:
Explanation
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

NEW QUESTION: 2
.......... topology is the name given to the mapping of network based on the path taken by the actual data.
A. Physical
B. Logical
C. Signal
D. Hybrid
Answer: B

NEW QUESTION: 3
사용자가 총 비용을 줄이기 위해 레거시 애플리케이션을 AWS 클라우드로 이동하려고 합니다. 모범 사례에 따라 가장 비용 효율적인 옵션은 무엇입니까?
A. Python과 같은 오픈 소스 언어로 레거시 애플리케이션을 다시 작성합니다.
B. 추가 할인을 제공하는 선불 결제로 데이터 센터 시설을 예약합니다.
C. 컴퓨팅 및 메모리 측면에서 과잉 프로비저닝을 방지하기 위해 Amazon EC2 인스턴스의 크기를 적절하게 조정합니다.
D. 관계형 데이터베이스를 Amazon DynamoDB로 마이그레이션
Answer: B

NEW QUESTION: 4
Exhibit:

Refer to the exhibit, Which type of attack is illustrated?
A. ARP spoofing
B. ICMP flood
C. CAM overflow
D. IP address spoofing
Answer: A