Consider a classification problem where we wish to determine if a human subject is likely to get a concussion in the next year. We use four features - X1 (Age), x2 (concussHistory), X3 (Favorite Sport), X4 (Gender). Each feature takes on one of a discrete number of values, shown below: Age Child Teen Adult concuss History Never Recent DecadesAgo FavoriteSport Boxing Golf Rugby Baseball Gender Male Female We wish to classify each user as either y'=LikelyConcuss or y'=NotLikelyConcuss. 1. How can the features above be transformed to use a logistic classifier? For each feature, use a transformation that reasonably captures the structure of the data while minimizing the number of parameters to learn. 2. How many parameters are required to learn a separating hyper-plane (w and any other necessary elements) for logistic classification with the features converted in question 1? (Work from your answer to question 1. If you could not figure out question 1, assume we have a new space of 8 continuous numeric features X1, X2, ..., X8 this may or may not be a valid result from question 2.) = 3. A vector x is said to have "unit magnitude" if VxT x = 1. Which of the following vectors has approximate unit magnitude, 0.8