The K-means algorithm:

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Question:
The K-means algorithm:

1. requires the dimension of the feature space to be no bigger than the number of samples

2. has the smallest value of the objective function when k = 1

3.minimizes the within class variance for a given number of clusters

4.converges to the global optimum if and only if the initial means are chosen as some of the samples themselves

Posted Date:-2022-06-22 08:50:41

More MCQS Questions and answers

  1. If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance?
  2. In Naive Bayes equation P(C / X)= (P(X / C) *P(C) ) / P(X) which part considers "likelihood"?
  3. In which neural net architecture, does weight sharing occur?
  4. Skewness of Normal distribution is ___________
  5. 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
  6. Which of the following is an application of NN (Neural Network)?
  7. Which of the following is true about Manhattan distance?
  8. Which one of the following is not a major strength of the neural network approach?
  9. Which Statement is not true statement.
  10. 8 observations are clustered into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations: C1: {(2,2), (4,4), (6,6)} C2: {(0,4), (4,0),(2,5)} C3: {(5,5), (9,9)} What will be the cluster centroids if you want to proceed for second iteration?
  11. A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong?Note: Model has successfully deployed and no technical issues are found at client side except the model performance
  12. Consider the following dataset. x,y,z are the features and T is a class(1/0). Classify the test data (0,0,1) as values of x,y,z respectively.
  13. Decision Tree is
  14. Generally, an ensemble method works better, if the individual base models have ____________?Note: Suppose each individual base models have accuracy greater than 50%.
  15. Give the correct Answer for following statements. 1. It is important to perform feature normalization before using the Gaussian kernel. 2. The maximum value of the Gaussian kernel is 1.
  16. Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.
  17. Hierarchical clustering is slower than non-hierarchical clustering?
  18. High entropy means that the partitions in classification are
  19. his 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:
  20. How many terms are required for building a bayes model?
  21. How the entries in the full joint probability distribution can be calculated?
  22. In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method.
  23. In which of the following cases will K-Means clustering fail to give good results?
  24. In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes
  25. Neural Networks are complex ______________ with many parameters.
  26. Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other
  27. Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate?
  28. The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?
  29. The F-test
  30. The K-means algorithm:
  31. The main disadvantage of maximum likelihood methods is that they are _____
  32. The maximum likelihood method can be used to explore relationships among more diverse sequences, conditions that are not well handled by maximum parsimony methods.
  33. The network that involves backward links from output to the input and hidden layers is called
  34. The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%. We also know that 3% of the adult population subscribes to automotive magazine. The probability of a person owning a sports car given that they don’t subscribe to automotive magazine is 30%. Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car
  35. Tree/Rule based classification algorithms generate ... rule to perform the classification.
  36. What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn d) None of the mentioned
  37. What is back propagation?
  38. what is Feature scaling done before applying K-Mean algorithm?
  39. What is gini index?
  40. What is the actual number of independent parameters which need to be estimated in P dimensional Gaussian distribution model?
  41. What is the naïve assumption in a Naïve Bayes Classifier.
  42. What is true about an ensembled classifier? 1. Classifiers that are more “sure” can vote with more conviction 2. Classifiers can be more “sure” about a particular part of the space 3. Most of the times, it performs better than a single classifier
  43. Which of the following are correct statement(s) about stacking? 1. A machine learning model is trained on predictions of multiple machine learning models 2. A Logistic regression will definitely work better in the second stage as compared to other classification methods 3. First stage models are trained on full / partial feature space of training data
  44. Which of the following can act as possible termination conditions in K-Means? 1. For a fixed number of iterations. 2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum. 3. Centroids do not change between successive iterations. 4. Terminate when RSS falls below a threshold.
  45. Which of the following can be true for selecting base learners for an ensemble? 1. Different learners can come from same algorithm with different hyper parameters 2. Different learners can come from different algorithms 3. Different learners can come from different training spaces
  46. Which of the following metrics, do we have for finding dissimilarity between two clusters in hierarchical clustering? 1. Single-link 2. Complete-link 3. Average-link
  47. Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance 2. Generalized models 3. Better interpretability
  48. Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance 2. Generalized models 3. Better interpretability
  49. Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features
  50. Which of the following statement is true about k-NN algorithm? 1) k-NN performs much better if all of the data have the same scale 2) k-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large 3) k-NN makes no assumptions about the functional form of the problem being solved
  51. Which of the following statements about Naive Bayes is incorrect?
  52. Which one of the following is the main reason for pruning a Decision Tree?
  53. Which statement is true about the K-Means algorithm? Select one:
  54. With Bayes theorem the probability of hypothesis H¾ specified by P(H) ¾ is referred to as
  55. You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem?
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