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NEW QUESTION: 1
The most appropriate method of evaluating whether the diet of a child with cystic fibrosis is meeting his caloric needs is:
A. Carefully recording amounts and types of foods ingested
B. Keeping a strict account of the number of calories ingested
C. Keeping a careful account of the amount of pancreatic enzymes ingested
D. Careful monitoring of weight loss or gain
Answer: D
Explanation:
Explanation
(A) Consistent weight gain, even if it is slow, is an indication that the child is eating and digesting sufficient calories. (B) Recording how much the child eats is useful, but it is not an indicator of how well his body is using the foods consumed. (C) Counting calories will indicate how much he is eating, but it will not reflect whether or not the foods are properly digested. (D) Keeping track of the enzyme intake will indicate compliance with medication but not whether the child is getting sufficient calories.
NEW QUESTION: 2
In der folgenden Tabelle sind die virtuellen Azure-Netzwerke aufgeführt.
Zu welchen virtuellen Netzwerken können Sie eine Peering-Verbindung von VNet1 aus herstellen?
A. Nur VNet2 und VNet3
B. Nur VNet3 und VNet4
C. Nur VNet2
D. VNet2, VNet3 und VNet4
Answer: D
Explanation:
Erläuterung:
Verweise:
https://docs.microsoft.com/en-us/azure/virtual-network/tutorial-connect-virtual-networks-portal
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:
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
NEW QUESTION: 4
Which of the following are the Boolean logic functions that can be used to create Correlation Rules?
A. NOR and AND
B. OR and AND
C. AND and SET
D. OR and SET
Answer: B
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