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 (FavoriteSport), x4 (Gender) . Each feature takes on one of a discrete number of values, shown below:

Age: Child, Teen, Adult

concussHistory: Never, Recent, DecadesAgo

FavoriteSport: Boxing, Golf, Rugby, Baseball

Gender: Male, Female

We wish to classify each user as either y i=LikelyConcuss or yi=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