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NEW QUESTION: 1
Section B (2 Mark)
Which of the following Biases are exhibited by Active Accumulator?
A. III and IV
B. All of the Above
C. I, II and III
D. I and III
Answer: C
NEW QUESTION: 2
クライアントには、高度に仮想化されたサーバー環境用のデータセンターネットワークが必要です。次の要件があります。
*サーバーからトップオブラックスイッチへの25 Gb接続
* iSCSlストレージのサポート
* VMファーム間の可能な最小のレイテンシ。これらは別々のキャビネットに含まれ、データセンター内のさまざまなトップオブラックスイッチに接続されます。
* Pythonスクリプトの解釈とオープンAPIのサポート
どのネットワーキング製品ラインとアーキテクチャをお勧めしますか？
A. スパイン/リーフアーキテクチャで構成されたArubaOS-CX、スパインとリーフに8325スイッチ
B. スパイン/リーフアーキテクチャで構成されたArubaOS-Swrtchで、スパインに5406Rスイッチを、2930Mリーフスイッチを使用
C. コアに5950スイッチ、キャビネットごとにIRFスタックを使用して構成された5940アクセススイッチを備えた従来の2層アーキテクチャーで構成されたHPE FlexFabicc
D. スパイン/リーフアーキテクチャで構成されたArubaOS-CXで、スパインに8325スイッチ、キャビネット間でVSXを使用する8320リーフスイッチ
Answer: B
NEW QUESTION: 3
You are evaluating a Python NumPy array that contains six data points defined as follows:
data = [10, 20, 30, 40, 50, 60]
You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
You need to implement a cross-validation to generate the output.
How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
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