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NEW QUESTION: 1
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps.
Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Explanation:
Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
NEW QUESTION: 2
You have a Python data frame named salesData in the following format:
The data frame must be unpivoted to a long data format as follows:
You need to use the pandas.melt() function in Python to perform the transformation.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: dataFrame
Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source] Where frame is a DataFrame Box 2: shop Paramter id_vars id_vars : tuple, list, or ndarray, optional Column(s) to use as identifier variables.
Box 3: ['2017','2018']
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
Example:
df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
... 'B': {0: 1, 1: 3, 2: 5},
... 'C': {0: 2, 1: 4, 2: 6}})
pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6
References:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html
NEW QUESTION: 3
Conducting an inspection of source code is:
A. Quality Assurance
B. None of the above
C. Quality Control
D. Compilation
Answer: C
NEW QUESTION: 4
Sie haben 20 Computer, auf denen Windows 10 ausgeführt wird.
Sie konfigurieren alle Computer so, dass alle Ereignisse aus allen Protokollen an einen Computer mit dem Namen Computer1 weitergeleitet werden, auf dem Windows 10 ausgeführt wird.
Wenn Sie sich bei Computer1 anmelden, werden keine Sicherheitsereignisse von anderen Computern angezeigt. Sie können alle anderen weitergeleiteten Ereignisse von den anderen Computern anzeigen.
Sie müssen sicherstellen, dass die Sicherheitsereignisse an Computer1 weitergeleitet werden.
Was sollte man tun?
A. Fügen Sie auf jedem Computer das Konto NETWORK SERVICE zur Gruppe Event Log Readers hinzu.
B. Fügen Sie auf Computer1 das Konto von Computer1 zur Gruppe Ereignisprotokollleser hinzu.
C. Führen Sie auf jedem Computer winrm qc -q aus.
D. Führen Sie auf jedem Computer wecutil ac / q aus.
Answer: B
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/windows/security/threat-protection/use-windows-event-forwarding-to-assistin-i
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