McqMate
1. |
Application of machine learning methods to large databases is called |
A. | data mining. |
B. | artificial intelligence |
C. | big data computing |
D. | internet of things |
Answer» A. data mining. |
2. |
If machine learning model output involves target variable then that model is called as |
A. | descriptive model |
B. | predictive model |
C. | reinforcement learning |
D. | all of the above |
Answer» B. predictive model |
3. |
In what type of learning labelled training data is used |
A. | unsupervised learning |
B. | supervised learning |
C. | reinforcement learning |
D. | active learning |
Answer» B. supervised learning |
4. |
In following type of feature selection method we start with empty feature set |
A. | forward feature selection |
B. | backword feature selection |
C. | both a and b?? |
D. | none of the above |
Answer» A. forward feature selection |
5. |
In PCA the number of input dimensiona are equal to principal components |
A. | true |
B. | false |
Answer» A. true |
6. |
PCA can be used for projecting and visualizing data in lower dimensions. |
A. | true |
B. | false |
Answer» A. true |
7. |
Which of the following is the best machine learning method? |
A. | scalable |
B. | accuracy |
C. | fast |
D. | all of the above |
Answer» D. all of the above |
8. |
What characterize unlabeled examples in machine learning |
A. | there is no prior knowledge |
B. | there is no confusing knowledge |
C. | there is prior knowledge |
D. | there is plenty of confusing knowledge |
Answer» D. there is plenty of confusing knowledge |
9. |
What does dimensionality reduction reduce? |
A. | stochastics |
B. | collinerity |
C. | performance |
D. | entropy |
Answer» B. collinerity |
10. |
Data used to build a data mining model. |
A. | training data |
B. | validation data |
C. | test data |
D. | hidden data |
Answer» A. training data |
11. |
The problem of finding hidden structure in unlabeled data is called… |
A. | supervised learning |
B. | unsupervised learning |
C. | reinforcement learning |
D. | none of the above |
Answer» B. unsupervised learning |
12. |
Of the Following Examples, Which would you address using an supervised learning Algorithm? |
A. | given email labeled as spam or not spam, learn a spam filter |
B. | given a set of news articles found on the web, group them into set of articles about the same story. |
C. | given a database of customer data, automatically discover market segments and group customers into different market segments. |
D. | find the patterns in market basket analysis |
Answer» A. given email labeled as spam or not spam, learn a spam filter |
13. |
Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model |
A. | true |
B. | false |
Answer» A. true |
14. |
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 |
A. | supervised learning |
B. | unsupervised learning |
C. | semisupervised learning |
D. | reinforcement learning |
Answer» A. supervised learning |
15. |
Which of the following is a good test dataset characteristic? |
A. | large enough to yield meaningful results |
B. | is representative of the dataset as a whole |
C. | both a and b |
D. | none of the above |
Answer» C. both a and b |
16. |
Following are the types of supervised learning |
A. | classification |
B. | regression |
C. | subgroup discovery |
D. | all of the above |
Answer» D. all of the above |
17. |
Type of matrix decomposition model is |
A. | descriptive model |
B. | predictive model |
C. | logical model |
D. | none of the above |
Answer» A. descriptive model |
18. |
Following is powerful distance metrics used by Geometric model |
A. | euclidean distance |
B. | manhattan distance |
C. | both a and b?? |
D. | square distance |
Answer» C. both a and b?? |
19. |
The output of training process in machine learning is |
A. | machine learning model |
B. | machine learning algorithm |
C. | null |
D. | accuracy |
Answer» A. machine learning model |
20. |
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
|
A. | nominal |
B. | ordinal |
C. | categorical |
D. | boolean |
Answer» B. ordinal |
21. |
PCA is |
A. | forward feature selection |
B. | backword feature selection |
C. | feature extraction |
D. | all of the above |
Answer» C. feature extraction |
22. |
Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model. |
A. | true |
B. | false |
Answer» A. true |
23. |
Which of the following techniques would perform better for reducing dimensions of a data set? |
A. | removing columns which have too many missing values |
B. | removing columns which have high variance in data |
C. | removing columns with dissimilar data trends |
D. | none of these |
Answer» A. removing columns which have too many missing values |
24. |
Supervised learning and unsupervised clustering both require which is correct according to the statement. |
A. | output attribute. |
B. | hidden attribute. |
C. | input attribute. |
D. | categorical attribute |
Answer» C. input attribute. |
25. |
What characterize is hyperplance in geometrical model of machine learning? |
A. | a plane with 1 dimensional fewer than number of input attributes |
B. | a plane with 2 dimensional fewer than number of input attributes |
C. | a plane with 1 dimensional more than number of input attributes |
D. | a plane with 2 dimensional more than number of input attributes |
Answer» B. a plane with 2 dimensional fewer than number of input attributes |
26. |
Like the probabilistic view, the ________ view allows us to associate a probability of membership with each classification. |
A. | exampler |
B. | deductive |
C. | classical |
D. | inductive |
Answer» D. inductive |
27. |
Database query is used to uncover this type of knowledge. |
A. | deep |
B. | hidden |
C. | shallow |
D. | multidimensional |
Answer» D. multidimensional |
28. |
A person trained to interact with a human expert in order to capture their knowledge. |
A. | knowledge programmer |
B. | knowledge developer r |
C. | knowledge engineer |
D. | knowledge extractor |
Answer» D. knowledge extractor |
29. |
Some telecommunication company wants to segment their customers into distinct groups ,this is an example of |
A. | supervised learning |
B. | reinforcement learning |
C. | unsupervised learning |
D. | data extraction |
Answer» C. unsupervised learning |
30. |
In the example of predicting number of babies based on stork's population ,Number of babies is |
A. | outcome |
B. | feature |
C. | observation |
D. | attribute |
Answer» A. outcome |
31. |
Which learning Requires Self Assessment to identify patterns within data? |
A. | unsupervised learning |
B. | supervised learning |
C. | semisupervised learning |
D. | reinforced learning |
Answer» A. unsupervised learning |
32. |
Select the correct answers for following statements.
|
A. | both are true |
B. | 1 is true and 2 is false |
C. | both are false |
D. | 1 is false and 2 is true |
Answer» B. 1 is true and 2 is false |
33. |
The "curse of dimensionality" referes |
A. | all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions. |
B. | all the problems that arise when working with data in the lower dimensions, that did not exist in the higher dimensions. |
C. | all the problems that arise when working with data in the lower dimensions, that did not exist in the lower dimensions. |
D. | all the problems that arise when working with data in the higher dimensions, that did not exist in the higher dimensions. |
Answer» A. all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions. |
34. |
In simple term, machine learning is |
A. | training based on historical data |
B. | prediction to answer a query |
C. | both a and b?? |
D. | automization of complex tasks |
Answer» C. both a and b?? |
35. |
If machine learning model output doesnot involves target variable then that model is called as |
A. | descriptive model |
B. | predictive model |
C. | reinforcement learning |
D. | all of the above |
Answer» A. descriptive model |
36. |
Following are the descriptive models |
A. | clustering |
B. | classification |
C. | association rule |
D. | both a and c |
Answer» D. both a and c |
37. |
Different learning methods does not include? |
A. | memorization |
B. | analogy |
C. | deduction |
D. | introduction |
Answer» D. introduction |
38. |
A measurable property or parameter of the data-set is |
A. | training data |
B. | feature |
C. | test data |
D. | validation data |
Answer» B. feature |
39. |
Feature can be used as a |
A. | binary split |
B. | predictor |
C. | both a and b?? |
D. | none of the above |
Answer» C. both a and b?? |
40. |
It is not necessary to have a target variable for applying dimensionality reduction algorithms |
A. | true |
B. | false |
Answer» A. true |
41. |
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 |
A. | 1 & 2 |
B. | 2 & 3 |
C. | 3 & 4 |
D. | all of the above |
Answer» D. all of the above |
42. |
Which of the following is a reasonable way to select the number of principal components "k"? |
A. | choose k to be the smallest value so that at least 99% of the varinace is retained. - answer |
B. | choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer). |
C. | choose k to be the largest value so that 99% of the variance is retained. |
D. | use the elbow method |
Answer» A. choose k to be the smallest value so that at least 99% of the varinace is retained. - answer |
43. |
Which of the folllowing is an example of feature extraction? |
A. | construction bag of words from an email |
B. | applying pca to project high dimensional data |
C. | removing stop words |
D. | forward selection |
Answer» B. applying pca to project high dimensional data |
44. |
Prediction is |
A. | the result of application of specific theory or rule in a specific case |
B. | discipline in statistics used to find projections in multidimensional data |
C. | value entered in database by expert |
D. | independent of data |
Answer» A. the result of application of specific theory or rule in a specific case |
45. |
You are given sesimic data and you want to predict next earthquake , this is an example of |
A. | supervised learning |
B. | reinforcement learning |
C. | unsupervised learning |
D. | dimensionality reduction |
Answer» A. supervised learning |
46. |
PCA works better if there is
|
A. | 1 and 2 |
B. | 2 and 3 |
C. | 1 and 3 |
D. | 1,2 and 3 |
Answer» C. 1 and 3 |
47. |
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? |
A. | variable f1 is an example of nominal variable |
B. | variable f1 is an example of ordinal variable |
C. | it doesn\t belong to any of the mentioned categories |
D. | it belongs to both ordinal and nominal category |
Answer» B. variable f1 is an example of ordinal variable |
48. |
What can be major issue in Leave-One-Out-Cross-Validation(LOOCV)? |
A. | low variance |
B. | high variance |
C. | faster runtime compared to k-fold cross validation |
D. | slower runtime compared to normal validation |
Answer» B. high variance |
49. |
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? |
A. | classification |
B. | regression |
C. | kmeans algorithm |
D. | reinforcement learning |
Answer» D. reinforcement learning |
50. |
Support Vector Machine is |
A. | logical model |
B. | proababilistic model |
C. | geometric model |
D. | none of the above |
Answer» C. geometric model |
51. |
In multiclass classification number of classes must be |
A. | less than two |
B. | equals to two |
C. | greater than two |
D. | option 1 and option 2 |
Answer» C. greater than two |
52. |
Which of the following can only be used when training data are linearlyseparable? |
A. | linear hard-margin svm |
B. | linear logistic regression |
C. | linear soft margin svm |
D. | the centroid method |
Answer» A. linear hard-margin svm |
53. |
Impact of high variance on the training set ? |
A. | overfitting |
B. | underfitting |
C. | both underfitting & overfitting |
D. | depents upon the dataset |
Answer» A. overfitting |
54. |
What do you mean by a hard margin? |
A. | the svm allows very low error in classification |
B. | the svm allows high amount of error in classification |
C. | both 1 & 2 |
D. | none of the above |
Answer» A. the svm allows very low error in classification |
55. |
The effectiveness of an SVM depends upon: |
A. | selection of kernel |
B. | kernel parameters |
C. | soft margin parameter c |
D. | all of the above |
Answer» A. selection of kernel |
56. |
What are support vectors? |
A. | all the examples that have a non-zero weight ??k in a svm |
B. | the only examples necessary to compute f(x) in an svm. |
C. | all of the above |
D. | none of the above |
Answer» C. all of the above |
57. |
A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0. |
A. | true |
B. | false |
C. | sometimes – it can also output intermediate values as well |
D. | can’t say |
Answer» A. true |
58. |
What is the purpose of the Kernel Trick? |
A. | to transform the data from nonlinearly separable to linearly separable |
B. | to transform the problem from regression to classification |
C. | to transform the problem from supervised to unsupervised learning. |
D. | all of the above |
Answer» A. to transform the data from nonlinearly separable to linearly separable |
59. |
Which of the following can only be used when training data are linearlyseparable? |
A. | linear hard-margin svm |
B. | linear logistic regression |
C. | linear soft margin svm |
D. | parzen windows |
Answer» A. linear hard-margin svm |
60. |
The firing rate of a neuron |
A. | determines how strongly the dendrites of the neuron stimulate axons of neighboring neurons |
B. | is more analogous to the output of a unit in a neural net than the output voltage of the neuron |
C. | only changes very slowly, taking a period of several seconds to make large adjustments |
D. | can sometimes exceed 30,000 action potentials per second |
Answer» B. is more analogous to the output of a unit in a neural net than the output voltage of the neuron |
61. |
Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target? |
A. | auc-roc |
B. | accuracy |
C. | logloss |
D. | mean-squared-error |
Answer» D. mean-squared-error |
62. |
The cost parameter in the SVM means: |
A. | the number of cross-validations to be made |
B. | the kernel to be used |
C. | the tradeoff between misclassification and simplicity of the model |
D. | none of the above |
Answer» C. the tradeoff between misclassification and simplicity of the model |
63. |
The kernel trick |
A. | can be applied to every classification algorithm |
B. | is commonly used for dimensionality reduction |
C. | changes ridge regression so we solve a d ?? d linear system instead of an n ?? n system, given n sample points with d features |
D. | exploits the fact that in many learning algorithms, the weights can be written as a linear combination of input points |
Answer» D. exploits the fact that in many learning algorithms, the weights can be written as a linear combination of input points |
64. |
How does the bias-variance decomposition of a ridge regression estimator compare with that of ordinary least squares regression? |
A. | ridge has larger bias, larger variance |
B. | ridge has smaller bias, larger variance |
C. | ridge has larger bias, smaller variance |
D. | ridge has smaller bias, smaller variance |
Answer» C. ridge has larger bias, smaller variance |
65. |
Which of the following are real world applications of the SVM? |
A. | text and hypertext categorization |
B. | image classification |
C. | clustering of news articles |
D. | all of the above |
Answer» D. all of the above |
66. |
How can SVM be classified? |
A. | it is a model trained using unsupervised learning. it can be used for classification and regression. |
B. | it is a model trained using unsupervised learning. it can be used for classification but not for regression. |
C. | it is a model trained using supervised learning. it can be used for classification and regression. |
D. | t is a model trained using unsupervised learning. it can be used for classification but not for regression. |
Answer» C. it is a model trained using supervised learning. it can be used for classification and regression. |
67. |
Which of the following can help to reduce overfitting in an SVM classifier? |
A. | use of slack variables |
B. | high-degree polynomial features |
C. | normalizing the data |
D. | setting a very low learning rate |
Answer» A. use of slack variables |
68. |
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 is best option would you more likely to consider iterating SVM next time? |
A. | you want to increase your data points |
B. | you want to decrease your data points |
C. | you will try to calculate more variables |
D. | you will try to reduce the features |
Answer» C. you will try to calculate more variables |
69. |
What is/are true about kernel in SVM? 1. Kernel function map low dimensional data to high dimensional space 2. It’s a similarity function |
A. | 1 |
B. | 2 |
C. | 1 and 2 |
D. | none of these |
Answer» C. 1 and 2 |
70. |
You trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. Which is false. |
A. | this is an instance of overfitting |
B. | this is an instance of underfitting |
C. | the training was not well regularized |
D. | the training and testing examples are sampled from different distributions |
Answer» B. this is an instance of underfitting |
71. |
Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance? |
A. | increase the amount of traning data in each traning set |
B. | improve the optimization algorithm being used for error minimization. |
C. | decrease the model complexity |
D. | reduce the noise in the training data |
Answer» B. improve the optimization algorithm being used for error minimization. |
72. |
Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify? |
A. | the model would consider even far away points from hyperplane for modeling |
B. | the model would consider only the points close to the hyperplane for modeling |
C. | the model would not be affected by distance of points from hyperplane for modeling |
D. | none of the above |
Answer» B. the model would consider only the points close to the hyperplane for modeling |
73. |
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
|
A. | 1 |
B. | 1 and 2 |
C. | 1 and 3 |
D. | 2 and 3 |
Answer» B. 1 and 2 |
74. |
Wrapper methods are hyper-parameter selection methods that |
A. | should be used whenever possible because they are computationally efficient |
B. | should be avoided unless there are no other options because they are always prone to overfitting. |
C. | are useful mainly when the learning machines are “black boxes” |
D. | should be avoided altogether. |
Answer» C. are useful mainly when the learning machines are “black boxes” |
75. |
Which of the following methods can not achieve zero training error on any linearly separable dataset? |
A. | decision tree |
B. | 15-nearest neighbors |
C. | hard-margin svm |
D. | perceptron |
Answer» B. 15-nearest neighbors |
76. |
Suppose we train a hard-margin linear SVM on n > 100 data points in R2, yielding a hyperplane with exactly 2 support vectors. If we add one more data point and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n + 1 points are linearly separable)? |
A. | 2 |
B. | 3 |
C. | n |
D. | n+1 |
Answer» D. n+1 |
77. |
Let S1 and S2 be the set of support vectors and w1 and w2 be the learnt weight vectors for a linearly
|
A. | s1 ⚂ s2 |
B. | s1 may not be a subset of s2 |
C. | w1 = w2 |
D. | all of the above |
Answer» B. s1 may not be a subset of s2 |
78. |
Which statement about outliers is true? |
A. | outliers should be part of the training dataset but should not be present in the test data |
B. | outliers should be identified and removed from a dataset |
C. | the nature of the problem determines how outliers are used |
D. | outliers should be part of the test dataset but should not be present in the training data |
Answer» C. the nature of the problem determines how outliers are used |
79. |
If TP=9 FP=6 FN=26 TN=70 then Error rate will be |
A. | 45 percentage |
B. | 99 percentage |
C. | 28 percentage |
D. | 20 perentage |
Answer» C. 28 percentage |
80. |
Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case?
|
A. | 1 and 3 |
B. | 1 and 4 |
C. | 2 and 3 |
D. | 2 and 4 |
Answer» A. 1 and 3 |
81. |
he 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? |
A. | large datasets |
B. | small datasets |
C. | medium sized datasets |
D. | size does not matter |
Answer» A. large datasets |
82. |
Perceptron Classifier is |
A. | unsupervised learning algorithm |
B. | semi-supervised learning algorithm |
C. | supervised learning algorithm |
D. | soft margin classifier |
Answer» C. supervised learning algorithm |
83. |
Type of dataset available in Supervised Learning is |
A. | unlabeled dataset |
B. | labeled dataset |
C. | csv file |
D. | excel file |
Answer» B. labeled dataset |
84. |
which among the following is the most appropriate kernel that can be used with SVM to separate the classes. |
A. | linear kernel |
B. | gaussian rbf kernel |
C. | polynomial kernel |
D. | option 1 and option 3 |
Answer» B. gaussian rbf kernel |
85. |
The SVMs are less effective when |
A. | the data is linearly separable |
B. | the data is clean and ready to use |
C. | the data is noisy and contains overlapping points |
D. | option 1 and option 2 |
Answer» C. the data is noisy and contains overlapping points |
86. |
Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify? |
A. | the model would consider even far away points from hyperplane for modeling |
B. | the model would consider only the points close to the hyperplane for modeling |
C. | the model would not be affected by distance of points from hyperplane for modeling |
D. | opton 1 and option 2 |
Answer» B. the model would consider only the points close to the hyperplane for modeling |
87. |
What is the precision value for following confusion matrix of binary classification? |
A. | 0.91 |
B. | 0.09 |
C. | 0.9 |
D. | 0.95 |
Answer» B. 0.09 |
88. |
Which of the following are components of generalization Error? |
A. | bias |
B. | vaiance |
C. | both of them |
D. | none of them |
Answer» C. both of them |
89. |
Which of the following is not a kernel method in SVM? |
A. | linear kernel |
B. | polynomial kernel |
C. | rbf kernel |
D. | nonlinear kernel |
Answer» A. linear kernel |
90. |
During the treatement of cancer patients , the doctor needs to be very careful about which patients need to be given chemotherapy.Which metric should we use in order to decide the patients who should given chemotherapy? |
A. | precision |
B. | recall |
C. | call |
D. | score |
Answer» A. precision |
91. |
Which one of the following is suitable? 1. When the hypothsis space is richer, overfitting is more likely. 2. when the feature space is larger , overfitting is more likely. |
A. | true, false |
B. | false, true |
C. | true,true |
D. | false,false |
Answer» C. true,true |
92. |
Which of the following is a categorical data? |
A. | branch of bank |
B. | expenditure in rupees |
C. | prize of house |
D. | weight of a person |
Answer» A. branch of bank |
93. |
The soft margin SVM is more preferred than the hard-margin SVM when- |
A. | the data is linearly seperable |
B. | the data is noisy and contains overlapping points |
C. | the data is not noisy and linearly seperable |
D. | the data is noisy and linearly seperable |
Answer» B. the data is noisy and contains overlapping points |
94. |
In SVM which has quadratic kernel function of polynomial degree 2 that has slack variable C as one hyper paramenter. What would happen if we use very large value for C |
A. | we can still classify the data correctly for given setting of hyper parameter c |
B. | we can not classify the data correctly for given setting of hyper parameter c |
C. | we can not classify the data at all |
D. | data can be classified correctly without any impact of c |
Answer» A. we can still classify the data correctly for given setting of hyper parameter c |
95. |
In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. In this scenario |
A. | the decision boundry in the transformed feature space in non-linear |
B. | the decision boundry in the transformed feature space in linear |
C. | the decision boundry in the original feature space in not considered |
D. | the decision boundry in the original feature space in linear |
Answer» B. the decision boundry in the transformed feature space in linear |
96. |
Which of the following is true about SVM? 1. Kernel function map low dimensional data to high dimensional space. 2. It is a similarity Function |
A. | 1 is true, 2 is false |
B. | 1 is false, 2 is true |
C. | 1 is true, 2 is true |
D. | 1 is false, 2 is false |
Answer» C. 1 is true, 2 is true |
97. |
What is the Accuracy in percentage based on following confusion matrix of three class classification.
|
A. | 0.75 |
B. | 0.97 |
C. | 0.95 |
D. | 0.85 |
Answer» B. 0.97 |
98. |
Which of the following method is used for multiclass classification? |
A. | one vs rest |
B. | loocv |
C. | all vs one |
D. | one vs another |
Answer» A. one vs rest |
99. |
Based on survey , it was found that the probability that person like to watch serials is 0.25 and the probability that person like to watch netflix series is 0.43. Also the probability that person like to watch serials and netflix sereis is 0.12. what is the probability that a person doesn't like to watch either? |
A. | 0.32 |
B. | 0.2 |
C. | 0.44 |
D. | 0.56 |
Answer» C. 0.44 |
100. |
A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there? |
A. | 12 |
B. | 24 |
C. | 48 |
D. | 72 |
Answer» D. 72 |
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