ou are asked to compile a classification dataset containing information about houses and apartments in Amsterdam. The target class is the energy efficiency rating of the apartment (a letter from A to F). You have a number of people at your disposal who will inspect the houses and apartments and record whatever features you tell them to. Name three numeric features that you think are likely to be predictive for the given target label, and three categorical features, so six in total. For each feature, explain briefly why you think it is likely to be useful for this task. The more independent the features are the better (that is, they don't all measure broadly the same thing in a slightly different way).
ou are asked to compile a classification dataset containing information about houses and apartments in Amsterdam. The target class is the energy efficiency rating of the apartment (a letter from A to F). You have a number of people at your disposal who will inspect the houses and apartments and record whatever features you tell them to. Name three numeric features that you think are likely to be predictive for the given target label, and three categorical features, so six in total. For each feature, explain briefly why you think it is likely to be useful for this task. The more independent the features are the better (that is, they don't all measure broadly the same thing in a slightly different way).
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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You are asked to compile a classification dataset containing information about houses and apartments in Amsterdam. The target class is the energy efficiency rating of the apartment (a letter from A to F).
You have a number of people at your disposal who will inspect the houses and apartments and record whatever features you tell them to.
Name three numeric features that you think are likely to be predictive for the given target label, and three categorical features, so six in total. For each feature, explain briefly why you think it is likely to be useful for this task. The more independent the features are the better (that is, they don't all measure broadly the same thing in a slightly different way).
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