STORE-AND-FORWARD is the switching method that the switch will revert to when the error threshold is reached. This happens when the switch configuration includes a user-defined error threshold on a per-port basis.
Answer:
Option (D) is the correct answer of this question.
Explanation:
Moore's Law relates to Moore's theory that the number of transistors on a microchip doubles every two years while device costs are halved.
Interpretations of Moore's law assert the computing power doubles every 18 months.The Moore's Law theory states that development is exponential.Moore's Law states that every couple of years we should expect our computers to increase their speed and capacity and we'll pay less for them.h
Option(A),Option(B) ,Option(c) and option(E) do not belongs to Moore's law so these options are incorrect options.
Answer:
num_guesses = int(input())
user_guesses = []
for i in range(num_guesses):
x = int(input())
user_guesses.append(x)
print(user_guesses)
Explanation:
This solution is provided in Python
This line prompts the user for a number of guesses
num_guesses = int(input())
This line initializes an empty list
user_guesses = []
This loop lets user input each guess
for i in range(num_guesses):
This line takes user input for each guess
x = int(input())
This appends the input to a list
user_guesses.append(x)
This prints the user guesses
print(user_guesses)
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.
