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NEW QUESTION: 1
Which of the following would a team create as a result of using a change control system?
A. Impact analysis
B. Change requests
C. Approved changes
D. Signoff
Answer: C

NEW QUESTION: 2
A newborn infant is exhibiting signs of respiratory distress. Which of the following would the nurse recognize as the earliest clinical sign of respiratory distress?
A. Decreased respirations
B. Increased respirations
C. Sternal and subcostal retractions
D. Cyanosis
Answer: C
Explanation:
(A) Cyanosis is a late clinical sign of respiratory distress. (B) Rapid respirations are normal in a newborn. (C) The newborn has to exert an extra effort for ventilation, which is accomplished by using the accessory muscles of ventilation. The diaphragm and abdominal muscles are immature and weak in the newborn. (D) Decreased respirations are a late clinical sign. In the newborn, decreased respirations precede respiratory failure.

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