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NEW QUESTION: 1
Which of the documents below should be available as evidence of Service management planning and operations?
A. an Ishikawa (Fishbone) diagram
B. risk and impact checklists
C. policies and plans for service compliance
D. work instructions for storage monitoring
Answer: C

NEW QUESTION: 2
An organization is performing a Portfolio Analysis with Project Server 2013 to determine the projects that will be executed the next year. The company has the following base staff: one application developer, one business analyst, one project administrator, and one trainer.
The Resource Analysis is shown the following exhibit. (Click the Exhibit button.)

You need to include all projects and resolve the overallocations in the most cost-effective way.
What should you do?
A. Revise the primary cost constraint.
B. Hire an additional business analyst.
C. Enter new start dates.
D. Use the Force In/Out feature.
Answer: C

NEW QUESTION: 3
次のように定義された6つのデータポイントを含むPython NumPy配列を評価しています。
データ= [10、20、30、40、50、60]
Python Scikit-learn機械学習ライブラリのk-foldアルゴリズムの埋め込みを使用して、次の出力を生成する必要があります。
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
出力を生成するには、相互検証を実装する必要があります。
どのようにコードセグメントを完成させるべきですか？回答するには、回答領域のダイアログボックスで適切なコードセグメントを選択します。
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。

Answer:
Explanation:

Explanation

Box 1: k-fold
Box 2: 3
K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.
Box 3: data
Example: Example:
>>>
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
References:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html