# The data has features ID, Race, Age, Sex, Hour, IQ and Height with class variable Died. The aim is # to predict whether or not the given patient in the hospital will Die (TRUE) or not (FALSE). You may # or may not use all of the features in your models. > utils: ::menuInstallPkgs() # A window will open. Select wakefield. > library("wakefield") > set.seed (1337016) *'s. > data = r_data(500) # You generate a data set consisting of 500 instances Write it in program r using the logistic regression method

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
Chapter1: Introduction
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# The data has features ID, Race, Age, Sex, Hour, IQ and Height with class variable Died. The aim is
# to predict whether or not the given patient in the hospital will Die (TRUE) or not (FALSE). You may
# or may not use all of the features in your models.
> utils :::menuInstallPkgs() # A window will open. Select wakefield .
> library("wakefield")
> set.seed (1337016) *'s.
> data = r_data(500) # You generate a data set consisting of 500 instances
Write it in program r using the logistic regression method
Transcribed Image Text:# The data has features ID, Race, Age, Sex, Hour, IQ and Height with class variable Died. The aim is # to predict whether or not the given patient in the hospital will Die (TRUE) or not (FALSE). You may # or may not use all of the features in your models. > utils :::menuInstallPkgs() # A window will open. Select wakefield . > library("wakefield") > set.seed (1337016) *'s. > data = r_data(500) # You generate a data set consisting of 500 instances Write it in program r using the logistic regression method
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