M

Mr. Dubey • 52.61K Points
Coach 

Q. The K-means algorithm:

  • (A) requires the dimension of the feature space to be no bigger than the number of samples
  • (B) has the smallest value of the objective function when k = 1
  • (C) minimizes the within class variance for a given number of clusters
  • (D) converges to the global optimum if and only if the initial means are chosen as some of the samples themselves

No solution found for this question.
Add Solution and get +2 points.

You must be Logged in to update hint/solution

Discusssion

Login to discuss.

Related MCQs on Machine Learning

Q. Linear Regression is a supervised machine learning algorithm.

Q. The properties of the objects act like different kinds of class members. They are

Q. What is the purpose of the assign() method?

Q. Suppose we fit "Lasso Regression" to a data set, which has 100 features (X1,X2...X100). Now, we rescale one of these feature by multiplying with 10 (say that feature is X1), and then refit Lasso regression with the same regularization parameter. Now, which of the following option will be correct?

Q. Gini index does not favour equal sized partitions.

Q. If two variables are correlated, is it necessary that they have a linear relationship?

Q. Which of the following method is used to find the optimal features for cluster analysis

Q. The adjusted multiple coefficient of determination accounts for

Q. What is the sequence of the following tasks in a perceptron? Initialize weights of perceptron randomly Go to the next batch of dataset If the prediction does not match the output, change the weights For a sample input, compute an output

Q. How many Node object methods are available?

Learn All Machine Learning MCQs

Question analytics