I do not even know wht to tell u
I would honestly say that there are a few available tools that Carrie can use, but the best tool is the inbuilt Windows Powershell. As Powershell continues to extend its purpose and usefulness, Microsoft, on the other hand, continues to use Powershell's capability to develop more cmdlets for products like Windows Servers. Everything that can be done in a GUI environment can be done in Powershell. Carrie should be able to use Powershell to run things more efficiently from the command line without stepping a foot on the physical server. She will only need to access the server from her desk remotely, run a few commands, and that is it. Powershell command line is so powerful; it carries with it every troubleshooting pack that you can think about.
The product of the data scientist, who writes a Machine Learning (ML) algorithm using a large data set, is called a data-driven model.
A data-driven model generates insights and increases the efficiency of decision-making.
This implies that decisions are made based on the insights that the model by the Machine Learning algorithm produces.
Thus, Machine Learning algorithm or code helps entities to make insightful business decisions to increase efficiency and effectiveness.
Learn more about Machine Learning here at brainly.com/question/23738591
Answer:
b
Explanation:
First, we need to initialize the classifier.
Then, we are required to train the classifier.
The next step is to predict the target.
And finally, we need to evaluate the classifier model.
You will find different algorithms for solving the classification problem. Some of them are like decision tree classification etc.
However, you need to know how these classifier works. And its explained before:
You need to initialize the classifier at first.
All kinds of classifiers in the scikit-learn make use of the method fit(x,y) for fitting the model or the training for the given training set in level y.
The predict(x) returns the y which is the predicted label.And this is prediction.
For evaluating the classifier model- the score(x,y) gives back the certain score for a mentioned test data x as well as the test label y.
