McqMate
Sign In
Hamberger menu
McqMate
Sign in
Sign up
Home
Forum
Search
Ask a Question
Sign In
McqMate Copyright © 2024
→
Computer Science Engineering (CSE)
→
Machine Learning (ML)
→
Can we calculate the skewness of variabl...
Q.
Can we calculate the skewness of variables based on mean and median?
A.
true
B.
false
Answer» B. false
2.7k
1
Do you find this helpful?
18
View all MCQs in
Machine Learning (ML)
Discussion
D
Dipti
2 years ago
can you please explain the reason why it's false? I think it should be True.
0
Login to comment
Related MCQs
Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider? 1. I will add more variables 2. I will start introducing polynomial degree variables 3. I will remove some variables
scikit-learn offers the class , which is responsible for filling the holes using a strategy based on the mean, median, or frequency
scikit-learn offers the class______, which is responsible for filling the holes using a strategy based on the mean, median, or frequency
Suppose you are given ‘n’ predictions on test data by ‘n’ different models (M1, M2, …. Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models? Note: We are working on a regression problem 1. Median 2. Product 3. Average 4. Weighted sum 5. Minimum and Maximum 6. Generalized mean rule
Skewness of Normal distribution is ___________
This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration
This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration
This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration Select one:
This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.
What are the steps for using a gradient descent algorithm? 1)Calculate error between the actual value and the predicted value 2)Reiterate until you find the best weights of network 3)Pass an input through the network and get values from output layer 4)Initialize random weight and bias 5)Go to each neurons which contributes to the error and change its respective values to reduce the error