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NEW QUESTION: 1
A user is having problems printing. The technician believes the printer may be out of ink. Which of the following troubleshooting steps should be performed NEXT?
A. Establish a plan of action to resolve the problem
B. Verify full system functionality
C. Test the theory to determine cause
D. Implement the solution
Answer: C
NEW QUESTION: 2
What two enhancements are in VMFS6 but not in VMFS5? (Choose two.)
A. GPT storage device partitioning
B. Support for 4k native disks
C. Support for 512e disks
D. Automatic space reclamation
Answer: B,D
NEW QUESTION: 3
A. Option B
B. Option D
C. Option C
D. Option A
Answer: A,D
Explanation:
https://www.microsoft.com/ja-jp/dynamics/crm-customer-center/import-accounts-leads-or-other-datA.aspx
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