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NEW QUESTION: 1
What happens when you define Workforce Compensation Plan components but the employees to whom the compensation change is being administered does not have any component in his or her salary? (Choose the best answer.)
A. The first component is automatically mapped to the salary and the amount associated with this component is posted as a salary adjustment to the employee record.
B. The last component is automatically mapped to the salary and the amount associated with this component is posted as a salary adjustment to the employee record.
C. The component adjustment is not posted as a salary adjustment to the employee's record. While designing the compensation plan, it has to be kept in mind that the plan components must have a direct mapping with the employee's salary components.
D. The sum of all components identified to be posted as salary is posted as a single salary adjustment amount to the employee's salary record.
Answer: B
NEW QUESTION: 2
As part of its recent efforts to improve brand recognition, Universal Containers had upgraded all of its Communities to use Lightning templates. The next step will be to unify the branding across all of these Communities. What is an efficient way to accomplish this?
Select one or more of the following:
A. Ensure that all Communities are only using standard Lighting Components
B. Ensure that all Community managers use the same CSS standards in all pages
C. Create a standard theme that is used in all of the Communities
D. Use a shared Bootstrap CSS file in all of the Communities
Answer: C
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