IIT M DIPLOMA QUIZ2 EXAM QPG1 10 July 2022 - 10 Jul 2022

Which of the following could be the decision boundary learned by a hard-margin SVM? Choose the most appropriate option. NOTE: The dataset is the same for all four options.

Which of the following could be the vector of probabilities output by a softmax regression model for 5 classes? Note that the options are independent of each other.

Consider a logistic regression model that is trained on videos to detect objectionable content. Videos with objectionable content belong to the positive class (label 1). Harmless videos belong to the negative class (label 0). A good detector should be able to correctly identify almost all videos that are objectionable. If it incorrectly classifies even a single video that has inappropriate content in it, that could have serious consequences, as millions of people might end up watching it. In this process the detector may classify some harmless videos as belonging to the positive class. But that is a price we are willing to pay. How should we choose the threshold (for inference) of this logistic regression model?

Consider a binary classification task that has 2 features. Assume that we train a soft-margin, linear SVM (decision boundary is a line in 2D space). We know nothing about the distribution of points in the training set. The points need not be linearly separable. This model is now tested on the following dataset that has four points. Consider a binary classification task that has 2 features. Assume that we train a soft-margin, linear SVM (decision boundary is a line in 2D space). We know nothing about the distribution of points in the training set. The points need not be linearly separable. This model is now tested on the following dataset that has four points. What are the possible values of the accuracy of the model? All options are independent of each other. Assume that the decision boundary of the model does not pass through any one of the four points.

What is the predicted label of the test point if k = 1? The label is 1 for the positive class and –1 for the negative class.

How many points are at a distance of 2 from the test point? Note: (1) We use the Manhattan distance. (2) We want those points that are exactly 2 units away from the test point.