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