DPO MLDA 4 Classification with Separating Hyperplane

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?

What happens if a data point violates the constraints set by the maximal margin hyperplane optimization problem?

What is the main goal of developing a classifier based on the training data in the context of the text?

Why is the event of a point lying exactly on the hyperplane considered to occur with probability zero?

In the context of data classification using a separating hyperplane, what does it mean if β0 + β1 X1 + β2 X2 + · · · + βp Xp ≥ 0?

Why is it mentioned in the text that shifting or rotating the hyperplane can provide another classifying hyperplane?

When will there exist an infinite number of hyperplanes that can perfectly separate the data?

What criterion is used to choose the best separating line (hyperplane) between two different classes?

What loss function is typically used for classifiers that output a class?

Which type of loss function has good numerical properties due to being a continuous convex function?

In binary classification metrics, what is the ideal scenario for a confusion matrix?

Why does accuracy not work well for skewed (unbalanced) classes in binary classification?

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?

Which type of classifier metrics provide the number of correct predictions over the total population?