This is a set of 32 questions of Machine Learning MCQs. All questions and answers are based on our research and self-study. If you missed the latest MCQs post on our site please visit once.
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1.) If you have a basket of different fruit varieties with some prior information on the size, color, shape of each and every fruit. Which learning methodology is best applicable?
2.) If the outcome is binary(0/1), which model is to be applied?
3.) For which one of these relationships could we use regression analysis? Chose the correct one
4.) Which clustering technique requires prior knowledge of the number of clusters required?
5.) Which of the learning methodology applies conditional probability of all the variables with respective the dependent variable?
6.) Which methodology works with clear margins of separation points?
7.) Which model helps SVM to implement the algorithm in high dimensional space?
8.) Consider a regression equation, Now which of the following could not be answered by regression?
9.) Which of them, best represents the property of Kernel?
10.) The main difficulty with using a regression line to analyze these data is ____
11.) SVM uses which method for pattern analysis in High dimensional space?
12.) Which of the following is not example of Clustering?
13.) Which type of the clustering could handle Big Data?
14.) What are different types of Supervised learning
15.) Now Can you make a quick guess where the Decision tree will fall into __
16.) Correlation and regression are concerned with the relationship between _
17.) Does Logistic regression check for the linear relationship between dependent and independent variables ?
18.) What are the advantages of neural networks (i) ability to learn by example (ii) fault-tolerant (iii) suited for real-time operation due to their high ‘computational’ rates
19.) Kernel methods can be used for supervised and unsupervised problems
20.)While running the same algorithm multiple times, which algorithm produces the same results?
21.) In a scenario, where the statistical model describes random error or noise instead of underlying relationship, what happens
22.) The main problem with using a single regression line
23.) Do you think heuristic for rule learning and heuristics for decision trees are both same?
24.) The standard approach to supervised learning is to split the set of examples into the training set and the test
25.) The model in which one estimates the probability that the outcome variable assumes a certain value, rather than estimating the value itself.
26.) SVM will not perform well with large data set because (select the best answer)
27.) Objective of unsupervised data covers all these aspects except
28.) Which technique implicitly defines the class of possible patterns by introducing a notion of similarity between data?
29.) What is the benefit of Naïve Bayes?
30.) The model which is widely used for the classification is
31.) The correlation between two variables is given by r = 0.0. . Which means
32.) In the Kernel trick method, We do not need the coordinates of the data in the feature space
Answer Sheet of Machine Learning MCQs
|Question Number||Question Answer|
|4.)||Answer.) k means|
|8.)||Answer.) linear or non linear|
|10.)||Answer.) one or more|
|13.)||Answer.) k means|
|14.)||Answer.) regression and class|
|16.)||Answer.) 2 quant|
|25.)||Answer.) Multi Linear|
|26.)||Answer.) Classification becomes difficult|
|27.)||Answer.) find clusters of data|
|28.)||Answer.) Linear Regression|
|29.)||Answer.) can process|
|30.)||Answer.) Multi Linear|
|31.)||Answer.) All the points|
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