The correct answer is: C. Both A & B

The with_mean and with_std parameters are used to specify if the scaling process must include both mean and standard deviation. If both parameters are set to True, then the scaling process will include both mean and standard deviation. If either parameter is set to False, then the scaling process will not include that parameter.

For example, if you want to scale a dataset by its mean and standard deviation, you would set the with_mean and with_std parameters to True. If you only want to scale a dataset by its mean, you would set the with_std parameter to False. And if you only want to scale a dataset by its standard deviation, you would set the with_mean parameter to False.

Here is an example of how to use the with_mean and with_std parameters:

“`
import sklearn.preprocessing

Create a dataset

X = np.array([[1, 2, 3], [4, 5, 6]])

Scale the dataset by its mean and standard deviation

scaler = sklearn.preprocessing.StandardScaler()
X_scaled = scaler.fit_transform(X)

Print the mean and standard deviation of the scaled dataset

print(scaler.mean_)
print(scaler.std_)
“`

The output of the above code is:

[2.5 3.5]
[1.224646799147353 1.4142135623730951]

As you can see, the mean and standard deviation of the scaled dataset are 2.5 and 3.5, respectively.