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NEW QUESTION: 1



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

NEW QUESTION: 2
Which value can you modify to configure a specific interface as the preferred forwarding interface?
A. the port priority
B. the hello time
C. the interface number
D. the VLAN priority
Answer: A

NEW QUESTION: 3
2クラスのロジスティック回帰モデルを使用して、バイナリ分類を作成しています。
不均衡のモデル結果を評価する必要があります。
どの評価基準を使用する必要がありますか？
A. 相対絶対誤差
B. 相対二乗誤差
C. 平均絶対誤差
D. AUCカーブ
Answer: D
Explanation:
One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random guessing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binary-classification-model