1. Let’s say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data.
You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?
Correct : D. both a and b
2. Which of the following sentence is FALSE regarding regression?
Correct : D. it discovers causal
relationships.
3. scikit-learn also provides functions for creating
dummy datasets from scratch:
Correct : D. all above
4. which can accept a NumPy RandomState
generator or an integer seed.
Correct : B. random_state
5. In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at
least valid options
Correct : B. 2
6. is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.
Correct : A. removing the whole line
7. It's possible to specify if the scaling process must include both mean and standard deviation using the
parameters .
Correct : C. both a & b
8. Which of the following selects the best K high-score
features.
Correct : C. selectkbest
9. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and
variance with lambda.
Correct : C. in case of very large lambda; bias is high, variance is
low
10. What is/are true about ridge regression?
1. When lambda is 0, model works like linear regression model
2. When lambda is 0, model doesn’t work like linear regression model
3. When lambda goes to infinity, we get very, very small coefficients approaching 0
4. When lambda goes to infinity, we get very, very large coefficients approaching infinity
Correct : A. 1 and 3
11. Which of the following method(s) does not have
closed form solution for its coefficients?
Correct : B. lasso
12. Function used for linear regression in R is
Correct : A. lm(formula, data)
13. In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to
Correct : C. (y-intercept, slope)
14. Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.Which of the following is true for X1?
Correct : B. relation between the x1 and y is strong
15. We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean
training error?
Correct : D. can’t say
16. We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of
training data?
Correct : D. bias increases and variance decreases
17. Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables
Correct : A. 1 and 2
18. Problem: Players will play if weather is sunny. Is
this statement is correct?
Correct : A. true
19. Multinomial Naïve Bayes Classifier is
_ distribution
Correct : B. discrete
20. For the given weather data, Calculate probability
of not playing
Correct : C. 0.36
21. The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes
of datasets are not best suited for SVM’s?
Correct : A. large datasets
22. The effectiveness of an SVM depends upon:
Correct : D. all of the
above
23. What do you mean by generalization error in terms of the SVM?
Correct : B. how accurately the svm can predict outcomes for
unseen data
24. We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use
Gaussian kernel in SVM
Correct : B. 1 and 2
25. Support vectors are the data points that lie
closest to the decision surface.
Correct : A. true
26. Which of the following is not supervised
learning?
Correct : A. pca
27. Gaussian Naïve Bayes Classifier is
_ distribution
Correct : A. continuous
28. If I am using all features of my dataset and I achieve 100% accuracy on my training set, but
~70% on validation set, what should I look out
for?
Correct : C. overfitting
29. What is the purpose of performing cross- validation?
Correct : C. both a and b
30. Suppose you are using a Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.If you remove the following any one red points from the data. Does the decision
boundary will change?
Correct : A. yes
31. Linear SVMs have no hyperparameters that need
to be set by cross-validation
Correct : B. false
32. For the given weather data, what is the
probability that players will play if weather is sunny
Correct : D. 0.6
33. 100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being a
man
Correct : B. 0.2
34. Problem: Players will play if weather is sunny. Is t
Correct : A. true
35. For the given weather data, Calculate probability
Correct : B. 0.64
36. For the given weather data, Calculate probability
Correct : C. 0.36
37. For the given weather data, what is the probabilit
Correct : D. 0.6
38. 100 people are at party. Given data gives informa
Correct : B. 0.2
39. 100 people are at party. Given data gives informa
Correct : A. true
40. What do you mean by generalization error in terms of the SVM?
Correct : B. how accuratel
41. The effectiveness of an SVM depends upon:
Correct : D. all of the abov
42. Support vectors are the data points that lie closest to the decision
Correct : A. true
43. The SVM’s are less effective when:
Correct : C. the data is noisy and contains
44. Suppose you are using RBF kernel in SVM with high Gamma valu
Correct : B. uthe model wo
45. The cost parameter in the SVM means:
Correct : C. the tradeoff between misclassificati on and simplicity of
the model
46. If I am using all features of my dataset and I achieve 100% accura
Correct : C. overfitting
47. Which of the following are real world applications of the SVM?
Correct : D. all of the abov
48. Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting.Which of the following option would you more likely to consider iterating SVM next time?
Correct : C. you will try to c
49. We usually use feature normalization before using the Gaussian k
Correct : B. 1 and 2
50. Linear SVMs have no hyperparameters that need to be set by cross-valid
Correct : B. false
51. In a real problem, you should check to see if the SVM is separable and th
Correct : B. false
52. In reinforcement learning, this feedback is usually called as .
Correct : C. reward
53. In the last decade, many researchers started training
bigger and bigger models, built with several different layers that's why this approach is called .
Correct : A. deep learning
54. When it is necessary to allow the model to develop a generalization ability and avoid a common problem
called .
Correct : A. overfitting
55. Techniques involve the usage of both labeled and unlabeled data is called .
Correct : B. semi- supervised
56. Reinforcement learning is particularly efficient when .
Correct : D. all above
57. During the last few years, many algorithms have been applied to deep
neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.
Correct : D. none of above
58. if there is only a discrete number of possible outcomes (called categories),
the process becomes a .
Correct : B. classification.
59. Let’s say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data.
You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?
Correct : D. both a and b
60. scikit-learn also provides functions for creating dummy datasets from scratch:
Correct : D. all above
61. which can accept a NumPy RandomState generator or an integer seed.
Correct : B. random_state
62. In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at
least valid options
Correct : B. 2
63. It's possible to specify if the scaling process must include both mean and standard deviation using the parameters .
Correct : C. both a & b
64. Which of the following selects the best K high-score features.
Correct : C. selectkbest
65. We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?
Correct : D. can’t say
66. We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
Correct : D. bias increases and variance decreases
67. Problem: Players will play if weather is sunny. Is this statement is correct?
Correct : A. true
68. Multinomial Naïve Bayes Classifier is
_ distribution
Correct : B. discrete
69. The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM’s?
Correct : A. large datasets
70. We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
Correct : B. 1 and 2
71. Which of the following is not supervised learning?
Correct : A. pca
72. Gaussian Naïve Bayes Classifier is
_ distribution
Correct : A. continuous
73. If I am using all features of my dataset and I achieve 100% accuracy on my training set, but
~70% on validation set, what should I look out for?
Correct : C. overfitting
74. The cost parameter in the SVM means:
Correct : C. the tradeoff between misclassificati on and simplicity of the model
75. We usually use feature normalization before using the Gaussian k
Correct : B. 1 and 2
76. The effectiveness of an SVM depends upon:
Correct : D. all of the above
77. The process of forming general concept definitions from examples of concepts to be
learned.
Correct : C. induction
78. Computers are best at learning
Correct : A. facts.
79. Data used to build a data mining model.
Correct : B. training data
80. Supervised learning and unsupervised clustering both require at least one
Correct : A. hidden attribute.
81. Supervised learning differs from unsupervised clustering in that supervised learning requires
Correct : B. input attributes to be categorical.
82. A regression model in which more than one independent variable is used to predict the
dependent variable is called
Correct : C. an independent model
83. A term used to describe the case when the independent variables in a multiple regression model
are correlated is
Correct : C. multicollinearity
84. A multiple regression model has the form: y = 2 + 3x1 + 4x2. As x1 increases by 1 unit (holding x2
constant), y will
Correct : C. increase by 4 units
85. A multiple regression model has
Correct : B. more than one dependent variable
86. A measure of goodness of fit for the estimated regression equation is the
Correct : C. mean square due to regression
87. The adjusted multiple coefficient of determination accounts for
Correct : D. none of the above
88. The multiple coefficient of determination is computed by
Correct : C. dividing sst by sse
89. For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient of
determination is
Correct : B. 4.00
90. A nearest neighbor approach is best used
Correct : B. when irrelevant attributes have been removed from the data.
91. Another name for an output attribute.
Correct : B. independent variable
92. Classification problems are distinguished from estimation problems in that
Correct : C. classification problems do not allow an output attribute.
93. Which statement is true about prediction problems?
Correct : D. the resultant model is designed to classify current behavior.
94. Which of the following is a common use of unsupervised clustering?
Correct : A. detect outliers
95. The average positive difference between computed and desired outcome values.
Correct : D. mean positive error
96. Selecting data so as to assure that each class is properly represented in both the training and
test set.
Correct : B. stratification
97. The standard error is defined as the square root of this computation.
Correct : A. the sample variance divided by the total number of sample instances.
98. Data used to optimize the parameter settings of a supervised learner model.
Correct : D. validation
99. Bootstrapping allows us to
Correct : A. choose the same training instance several times.
100. The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?
Correct : C. as the value of one attribute decreases the value of the second attribute increases.