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NEW QUESTION: 1
If a behavior analyst states, "It took four attempts, but Johnny completed the puzzle," she is using which measure?
A. Trials to criterion
B. Whole interval
C. Frequency
D. Percent of occurrence
Answer: B

NEW QUESTION: 2
You have a debit amount of 50 and credit amount of 80 as balance on an account. This account is assigned to the assets side in a financial statement version, with the debit indicator and the credit. How is this shown on the balance sheet? Please choose the correct answer.
A. The balance is shown on the liabilities side.
B. 50 is shown on the liabilities side and 80 is shown on the assets side.
C. The balance is shown on the assets side.
D. 0 is shown on the assets side and 30 is shown on the liabilities side.
Answer: C

NEW QUESTION: 3
Ein IT-Governance-Gremium möchte feststellen, ob die Bereitstellung von IT-Services auf durchgängig effizienten und effektiven Prozessen basiert. Welcher der folgenden Ansätze wäre der BESTE?
A. Implementieren Sie eine Balanced Scorecard
B. Aktuelle und zukünftige Kapazität analysieren.
C. Bewertung von Key Performance Indicators (KPIs).
D. Führen Sie eine Lückenanalyse durch.
Answer: C

NEW QUESTION: 4
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