You are given sesimic data and you want to predict next earthquake , this is an example of

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Question:
You are given sesimic data and you want to predict next earthquake , this is an example of

1.supervised learning

2. reinforcement learning

3.unsupervised learning

4.dimensionality reduction

Posted Date:-2022-06-21 03:35:58

More MCQS Questions and answers

  1. Database query is used to uncover this type of knowledge.
  2. If machine learning model output doesnot involves target variable then that model is called as
  3. In following type of feature selection method we start with empty feature set
  4. In multiclass classification number of classes must be
  5. In the example of predicting number of babies based on stork's population ,Number of babies is
  6. PCA works better if there is 1. A linear structure in the data 2. If the data lies on a curved surface and not on a flat surface 3. If variables are scaled in the same uni
  7. A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is
  8. A measurable property or parameter of the data-set is
  9. A person trained to interact with a human expert in order to capture their knowledge.
  10. A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case?
  11. Application of machine learning methods to large databases is called
  12. Data used to build a data mining model.
  13. Different learning methods does not include?
  14. Feature can be used as a
  15. Following are the descriptive models
  16. Following are the types of supervised learning
  17. If machine learning model output involves target variable then that model is called as
  18. Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?
  19. Impact of high variance on the training set ?
  20. In simple term, machine learning is
  21. In what type of learning labelled training data is used
  22. Like the probabilistic view, the ________ view allows us to associate a probability of membership with each classification.
  23. Of the Following Examples, Which would you address using an supervised learning Algorithm?
  24. ollowing is powerful distance metrics used by Geometric model
  25. PCA is
  26. Prediction is
  27. Select the correct answers for following statements. 1. Filter methods are much faster compared to wrapper methods. 2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation.
  28. Some telecommunication company wants to segment their customers into distinct groups ,this is an example of
  29. Supervised learning and unsupervised clustering both require which is correct according to the statement.
  30. Support Vector Machine is
  31. The "curse of dimensionality" referes
  32. The effectiveness of an SVM depends upon:
  33. The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method2. It searches for the directions that data have the largest variance3. Maximum number of principal components <= number of features4. All principal components are orthogonal to each other
  34. The output of training process in machine learning is
  35. The problem of finding hidden structure in unlabeled data is called…
  36. Type of matrix decomposition model is
  37. What can be major issue in Leave-One-Out-Cross-Validation(LOOCV)?
  38. What characterize is hyperplance in geometrical model of machine learning?
  39. What characterize unlabeled examples in machine learning
  40. What do you mean by a hard margin?
  41. What does dimensionality reduction reduce?
  42. Which learning Requires Self Assessment to identify patterns within data?
  43. Which of the folllowing is an example of feature extraction?
  44. Which of the following can only be used when training data are linearlyseparable?
  45. Which of the following is a good test dataset characteristic?
  46. Which of the following is a reasonable way to select the number of principal components "k"?
  47. Which of the following is the best machine learning method?
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  49. You are given reviews of few netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of
  50. You are given sesimic data and you want to predict next earthquake , this is an example of
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