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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

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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?

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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

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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)?

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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)?

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