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NEW QUESTION: 1

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

NEW QUESTION: 2

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

NEW QUESTION: 3
分類タスクを解いています。
k分割交差検定を使用して、限られたデータサンプルでモデルを評価する必要があります。まず、kパラメータを分割数として構成します。
交差検定のkパラメーターを構成する必要があります。
どの値を使用する必要がありますか？
A. k = 0.9
B. k = 0.5
C. k = 10
D. k = 1
Answer: C
Explanation:
Leave One Out (LOO) cross-validation
Setting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.
LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance.
This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.