What is shattering a set of points? Explain VC dimension.

2 years ago
Machine Learning

In order to shatter a given configuration of points, a classifier must be able to, for all possible assignments of positive and negative for the points, perfectly partition the plane such that positive points are separated from negative points. For a configuration of n points, there are 2n possible assignments of positive or negative. 
When choosing a classifier, we need to consider the type of data to be classified and this can be known by VC dimension of a classifier. It is defined as cardinality of the largest set of points that the classification algorithm i.e. the classifier can shatter. In order to have a VC dimension of at least n, a classifier must be able to shatter a single given configuration of n points.

0
Sanisha Maharjan
Sanisha Maharjan
Jan 12, 2022
More related questions

1. What is Overfitting? And how do you ensure you’re not overfitting with a model?

2. What is the difference between Entropy and Information Gain?

3. List the advantages and disadvantages of using Neural Networks.

4. Describe a hash table.

5. What is the meshgrid () method and the contourf () method? State some usesof both.

6. What are some differences between a linked list and an array?

7. What are hyperparameters and how are they different from parameters?

8. What is the difference between a generative and discriminative model?

9. What is the significance of Gamma and Regularization in SVM?

10. Differentiate between Statistical Modeling and Machine Learning?

11. Mention why feature engineering is important in model building and list out some of the techniques used for feature engineering.

12. Mention some of the EDA Techniques?

13. How would you handle an imbalanced dataset?

14. List the advantages and limitations of the Temporal Difference Learning Method.

15. Model accuracy or Model performance? Which one will you prefer and why?

16. What’s the difference between probability and likelihood?

17. Explain the difference between Lasso and Ridge?

18. What is Naive Bayes? Why is it Naive?

19. What is Bayes’ Theorem? State at least 1 use case with respect to the machine learning context?

20. Keeping train and test split criteria in mind, is it good to perform scaling before the split or after the split? 

21. Explain the term instance-based learning.

22. Define and explain the concept of Inductive Bias with some examples.

23. State the limitations of Fixed Basis Function.

24. Name and define techniques used to find similarities in the recommendation system.

25. How do we deal with sparsity issues in recommendation systems? How do we measure its effectiveness? Explain. 

26. List all types of popular recommendation systems? Name and explain two personalized recommendation systems along with their ease of implementation. 

27. How can we use a dataset without the target variable into supervised learning algorithms? 

28. Name a popular dimensionality reduction algorithm.

29. Is it possible to test for the probability of improving model accuracy without cross-validation techniques? If yes, please explain.

30. Why boosting is a more stable algorithm as compared to other ensemble algorithms? 

31. What is OOB error and how does it occur? 

32. What are overfitting and underfitting? Why does the decision tree algorithm suffer often with overfitting problem?

33. What are ensemble models? Explain how ensemble techniques yield better learning as compared to traditional classification ML algorithms? 

34. What is Kernel Trick in an SVM Algorithm?

35. What are Kernels in SVM? List popular kernels used in SVM along with a scenario of their applications.

36. How does the SVM algorithm deal with self-learning? 

37. Explain Dependency Parsing in NLP?

38. Differentiate between K-Means and KNN algorithms?

39. Which machine learning algorithm is known as the lazy learner and why is it called so?

40. What does the term Variance Inflation Factor mean?

41. What could be the issue when the beta value for a certain variable varies way too much in each subset when regression is run on different subsets of the given dataset?

42. Why is logistic regression a type of classification technique and not a regression? Name the function it is derived from? 

43. When does the linear regression line stop rotating or finds an optimal spot where it is fitted on data? 

44. List all assumptions for data to be met before starting with linear regression.

45. What is target imbalance? How do we fix it? A scenario where you have performed target imbalance on data. Which metrics and algorithms do you find suitable to input this data onto? 

46. Differentiate between regression and classification.

47. What is Linear Regression?

48. How do we check the normality of a data set or a feature? 

49. List the most popular distribution curves along with scenarios where you will use them in an algorithm.

50. Explain the difference between Normalization and Standardization.

51. What is the difference between regularization and normalisation? 

52. You built a DL model, and while training it, you noticed that after a certain number of epochs, the accuracy is decreasing. What’s the problem and how to fix it?  The answer should be around overfitting.

53. Give me an example of supervised learning and another for unsupervised learning?

54. Suppose, you found that your model is suffering from high variance. Which algorithm do you think could handle this situation and why?

55. Both being tree-based algorithms, how is Random Forest different from Gradient Boosting Algorithm (GBM)?

56. Why do we need a validation set and a test set?

57. What is Overfitting in Machine Learning and how can you avoid?

58. We know that one hot encoding increases the dimensionality of a dataset, but label encoding doesn’t. How?

59. Why rotation is required in PCA? What will happen if you don’t rotate the components?

60. What is PCA in Machine Learning?

61. How do you handle the missing or corrupted data in a dataset?

62. Imagine, you are given a dataset consisting of variables having more than 30% missing values. Let’s say, out of 50 variables, 8 variables have missing values, which is higher than 30%. How will you deal with them?

63. Explain Logistic Regression.

64. When should you use classification over regression?

65. What do you understand by Type I and Type II errors?

66. Explain false negative, false positive, true negative, and true positive with a simple example.

67. What is Variance Inflation Factor?

68. What is epoch in Machine Learning?

69. What is entropy in Machine Learning?

70. What is cross-validation in Machine Learning?

71. What is Bayes’ theorem in Machine Learning?

72. What are the differences between Supervised and Unsupervised Machine Learning?

73. Give a popular application of machine learning that you see on day to day basis?

74. What are two techniques of Machine Learning ?

75. What is sequence learning?

76. What are the different categories you can categorized the sequence learning process?

77. What is PAC Learning?

78. What is batch statistical learning?

79. What are the areas in robotics and information processing where sequential prediction problem arises?

80. What are the different methods for Sequential Supervised Learning?

81. What are the components of relational evaluation techniques?

82. What are support vector machines?

83. What is dimension reduction in Machine Learning?

84. What is PCA, KPCA and ICA used for?

85. What is an Incremental Learning algorithm in ensemble?

86. What is bias-variance decomposition of classification error in ensemble method?

87. What is the general principle of an ensemble method and what is bagging and boosting in ensemble method?

88. What are the two paradigms of ensemble methods?

89. When to use ensemble learning?

90. Why ensemble learning is used?

91. What is ensemble learning?

92. What are the two classification methods that SVM ( Support Vector Machine) can handle?

93. Why instance based learning algorithm sometimes referred as Lazy learning algorithm?

94. What are Bayesian Networks (BN) ?

95. Explain the two components of Bayesian logic program?

96. What is Perceptron in Machine Learning?

97. What is the difference between heuristic for rule learning and heuristics for decision trees?

98. Which method is frequently used to prevent overfitting?

99. What are the two methods used for the calibration in Supervised Learning?

100. What is Model Selection in Machine Learning?

101. What is Inductive Logic Programming in Machine Learning?

102. What is Genetic Programming?

103. In what areas Pattern Recognition is used?

104. What are the advantages of Naive Bayes?

105. What is classifier in machine learning?

106. What is the difference between artificial learning and machine learning?

107. What is algorithm independent machine learning?

108. Explain what is the function of ‘Supervised Learning’?

109. Explain what is the function of ‘Unsupervised Learning’?

110. What is not Machine Learning?

111. List down various approaches for machine learning?

112. What is ‘Training set’ and ‘Test set’?

113. What is the standard approach to supervised learning?

114. What is the standard approach to supervised learning?

115. What are the three stages to build the hypotheses or model in machine learning?

116. What are the different Algorithm techniques in Machine Learning?

117. What are the five popular algorithms of Machine Learning?

118. What is inductive machine learning?

119. How can you avoid overfitting ?

120. Why overfitting happens?

121. What is ‘Overfitting’ in Machine learning?

122. Mention the difference between Data Mining and Machine learning?

123. What is Machine learning?

Questions Bank

View all Questions