Training Models

Home
Assessment

19 / 57

What will be the output of the following code snippet?

u = np.array([2,5]) 
v = np.array([3,1]) 
xcoords, ycoords = zip(u,v) 
print(xcoords)

XP

Taking you to the next exercise in seconds...

Want to create exercises like this yourself? Click here.

Note - Having trouble with the assessment engine? Follow the steps listed here

Result

Checking Please wait.

Success

Error

No hints are availble for this assesment

Answer is not availble for this assesment

Previous Index Next

Please login to comment

0 Comments

There are 5 new comments.

Training Models

1 Machine Learning Training Models Part-1

2 Machine Learning Training Models Part-2

3 Machine Learning Training Models Part-3

4 Machine Learning Training Models Part-4

5 Machine Learning Training Models Part-5

6 Training Models - MCQs - Which of these will have a better chance of giving...

7 Training Models - MCQs - Which of these can be used to reduce the Irreducible...

8 Training Models - MCQs - Which of these is faster for large number of datasets?...

9 Training Models - MCQs - Which of these will you use when there are small...

10 Training Models - MCQs - Which of these will you use when there are large...

11 Training Models - MCQs - Batch Gradient Descent involves calculations over the full training set...

12 Training Models - MCQs - For linear regression problems, MSE is a convex function and...

13 Training Models - MCQs - Training Linear Regression model using Normal Equation is linear with...

14 Training Models - MCQs - Prediction using Linear Regression model is linear with both the...

15 Training Models - MCQs - Which of these is more prone to overfitting?...

16 Training Models - MCQs - RMSE is more sensitive to outliers than MAE. True or...

17 Training Models - MCQs - The higher the norm index would imply:...

18 Training Models - MCQs - Logitic regression is a...

19 Training Models - MCQs - What will be the output of the following code snippet?

20 Training Models - MCQs - Which of the following libraries can be used for linear...

21 Training Models - MCQs - Vector addition is commutative and associative?...

22 Training Models - MCQs - What is the dot product impelementation in numpy?

23 Training Models - MCQs - u = [1,2,3] and v = [3,4,5]. u * v...

24 Training Models - MCQs - Dot product is commutative...

25 Training Models - MCQs - Dot product between a vector and a scalar is not...

26 Training Models - MCQs - Is dot product of three vectors associative?...

27 Training Models - MCQs - If u.v = 0 where u and v are vectors,...

28 Training Models - MCQs - If u.v = 0 where u and v are vectors,...

29 Training Models - MCQs - Ridge regression uses which norm?...

30 Training Models - MCQs - Lasso regression uses which norm?...

31 Training Models - MCQs - Elastic Net uses which norm?...

32 Training Models - MCQs - For SGD, the solution is good but not optimal, true...

33 Training Models - MCQs - Which of these should be used for small training datasets?...

34 Training Models - MCQs - Which of these is an iterative way of training a...

35 Training Models - MCQs - In the normal equation, what is the size of of...

36 Training Models - MCQs - In the normal equation, we need to calculate the inverse...

37 Training Models - MCQs - In the normal equation, the computational complexity of calculating the...

38 Training Models - MCQs - In the normal equation, the computational complexity of calculating the...

39 Training Models - MCQs - Should we use normal equation for solving linear models with...

40 Training Models - MCQs - Should we use Batch Gradient Descent for solving linear models...

41 Training Models - MCQs - Batch Gradient Descent scales well with number of training datasets?...

42 Training Models - MCQs - Batch Gradient Descent scales well with number of features?...

43 Training Models - MCQs - How to determine a good learning rate for batch gradient...

44 Training Models - MCQs - In Batch Gradient Descent, convergence rate decreases with increase in...

45 Training Models - MCQs - A high-degree polynomial model is likely to have:...

46 Training Models - MCQs - In SGD, the solution never settles down. To solve this...

47 Training Models - MCQs - Which of the following Scikit class can be used for...

48 Training Models - MCQs - In mini-batch gradient descent, the path taken is less erratic...

49 Training Models - MCQs - It is harder for mini-batch gradient descent than SGD to...

50 Training Models - MCQs - NormalEquation class in scikit learn solve linear regression using:...

51 Training Models - MCQs - Which of these provide out-of-core support for linear regression?...

52 Training Models - MCQs - What will be the number of features after using Polynomial...

53 Training Models - MCQs - A high-degree polynomial feature can lead to:...

54 Training Models - MCQs - In over-fitting, the performance of the model generalizes well. True...

55 Training Models - MCQs - One way to improve an overfitting model is:...

56 Training Models - MCQs - A model's generalization error can expressed as a sum of...

57 Training Models - MCQs - A high-bias model is most like to:...