Please written by computer source 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.)

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
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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.)

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