Consider a classification problem where we wish to determine if a human subject is likely to have a heart attack in the next year. We use four features - x1 (Age), x2 (hospHistory), x3 (FavoriteFood), and x4 (Gender). Each feature takes on one of a discrete number of values, shown below:

Age:	Child	Teen	Adult	SeniorCitizen
hospHistory	Never	Recent	DecadesAgo
FavoriteFood	Apple,	Steak	Pasta	Ice Cream
Gender:	Male	Female

We wish to classify each user as either yi=LikelyAttack or yi=NotLikelyAttack.

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.)