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A patient goes to a doctor with symptoms S1, S2 and S3. The doctor suspects disease D1 and D2 and constructs a Bayesian network for the relation among the disease and symptoms as the following:
76. What is the Markov blanket of variable, S3
D1
D2
D1 and D2
None
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77. Consider the data-points in the figure below. Let us assume that the black-colored circles represent positive class whereas the white-colored circles represent negative class. Which of the following among H1, H2 and H3 is maximum-margin hyperplane?
H1
H2
H3
None of these
78. The soft margin SVM is more preferred than the hard-margin svm when: 1. The data is linearly separable 2. The data is noisy and contains overlapping point
1 only
2 only
1 and 2
79. Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter. What would happen when you use very large value of C (C->infinity)?
We can still classify data correctly for given setting of hyper parameter C.
We can not classify data correctly for given setting of hyper parameter C.
Can't say
None of the above
80. Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter. What would happen when you use very small C (C∼0)?
Data will be correctly classified
Misclassification would happen
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