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NEW QUESTION: 1
A. Option E
B. Option D
C. Option B
D. Option C
E. Option A
Answer: E
NEW QUESTION: 2
A. Telnet
B. C.Ps Exec
C. Windows PowerShell remoting
D. Remote Desktop
Answer: A
Explanation:
Explanation
https://technet.microsoft.com/en-us/library/bb491013.aspx
NEW QUESTION: 3
Examine the structure of the MARKStable:
Which two statements would execute successfully? (Choose two.)
SELECT SUM(DISTINCT NVL(subject1,0)), MAX(subject1)
A. FROM marks
WHERE subject1 > AVG(subject1);
SELECT SUM(subject1+subject2+subject3)
B. FROM marks
WHERE student_name LIKE 'R%';
C. FROM marks
WHERE student_name IS NULL;
SELECT student_name,SUM(subject1)
D. FROM marks
WHERE subject1 > subject2;
SELECT student_name subject1
Answer: C,D
NEW QUESTION: 4
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:
You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0)
Penalty parameter C of the error term.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html