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NEW QUESTION: 1
Welche der folgenden Aussagen ist wahr?
A. Regressionstests können in jeder Teststufe verwendet werden, müssen jedoch nach dem White-Box-Test durchgeführt werden
fertig.
B. Regressionstests sollen Nebenwirkungen feststellen, nachdem Änderungen am System unter vorgenommen wurden
Prüfung.
C. Regressionstests sind eine Technik nur für Systemtests und Abnahmetests.
D. Regressionstests sind ein alternativer Begriff für erneute Tests
Answer: C

NEW QUESTION: 2
As a consultant in your company, you are required to set up names and details of schools, colleges, universities, and so on, so that users can select from this list when entering their qualifications such as degrees.
Identify the correct setup task in Functional Setup Manager > Define Workforce Profiles.
A. Define Talent Profiles > Manage Profile Types
B. Define Talent Profile Content > Manage Content Subscribers
C. Define Talent Profile Content > Manage Educational Establishments
D. Define Talent Profile Content > Manage Profile Content Items
Answer: C

NEW QUESTION: 3
Which guest operating system supports PVRDMA?
A. Windows (64-Bit)
B. Linux (64-Bit)
C. Windows (32-Bit)
D. Linux (32-Bit)
Answer: B
Explanation:
The guest operating system that supports PVRDMA is Linux 64 bit.
Reference https://docs.vmware.com/en/VMware-
vSphere/6.5/com.vmware.vsphere.networking.doc/GUID-4A5EBD44-FB1E-4A83-BB47-
BBC65181E1C2.html

NEW QUESTION: 4
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

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