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Questions and Answers
What is the main benefit of having normalized coefficients in the context of hyperplane classification?
In the context of maximal margin classifier, what does it mean when a data point is linearly separable?
What is the role of the parameter M in the optimization problem for finding the maximal margin hyperplane?
How would increasing the number of dimensions in the feature space impact the complexity of finding the maximal margin hyperplane?
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What happens if a data point violates the constraints set by the maximal margin hyperplane optimization problem?
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What is the main goal of developing a classifier based on the training data in the context of the text?
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Why is the event of a point lying exactly on the hyperplane considered to occur with probability zero?
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In the context of data classification using a separating hyperplane, what does it mean if β0 + β1 X1 + β2 X2 + · · · + βp Xp ≥ 0?
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Why is it mentioned in the text that shifting or rotating the hyperplane can provide another classifying hyperplane?
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When will there exist an infinite number of hyperplanes that can perfectly separate the data?
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What criterion is used to choose the best separating line (hyperplane) between two different classes?
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What loss function is typically used for classifiers that output a class?
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Which type of loss function has good numerical properties due to being a continuous convex function?
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In binary classification metrics, what is the ideal scenario for a confusion matrix?
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Why does accuracy not work well for skewed (unbalanced) classes in binary classification?
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In a dataset with 1000 emails, where 950 are spam and 50 are not spam, if a model predicts 'spam' for all emails, what is the accuracy?
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Which type of classifier metrics provide the number of correct predictions over the total population?
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