ComputerScienceExpert

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About ComputerScienceExpert

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Elementary,Middle School,High School,College,University,PHD

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Applied Sciences,Calculus See all
Applied Sciences,Calculus,Chemistry,Computer Science,Environmental science,Information Systems,Science Hide all
Teaching Since: Apr 2017
Last Sign in: 103 Weeks Ago, 3 Days Ago
Questions Answered: 4870
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Education

  • MBA IT, Mater in Science and Technology
    Devry
    Jul-1996 - Jul-2000

Experience

  • Professor
    Devry University
    Mar-2010 - Oct-2016

Category > Programming Posted 22 May 2017 My Price 8.00

INSY 5339HW #5: Predict Treatment

INSY 5339HW #5: Predict Treatment OutcomeUse the same genes-leukemia.csv dataset used in assignment #2.As a predictor use field TREATMENT_RESPONSE, which has values Success, Failureor "?" (missing)Step 1. Examine the records where TREATMENT_RESPONSE is non-missing.Q1: How many such records are there?Q2: Can you describe these records using other sample fields (e.g. Year from XXXX toYYYY , or Gender = X, etc)Q3: Why is it not correct to build predictive models for TREATMENT_RESPONSEusing records where it is missing?Step 2. Select only the records with non-missing TREATMENT_RESPONSE. KeepSNUM (sample number) but remove sample fields that are all the same or missing (i.e.,they have same value for 10 or more samples). Call the reduced dataset genesreduced.csvQ4: How many sample fields should you keep?Step 3. Build a J48 Model using leave-one-out cross validation (which is same as n-foldcross-validation where n is the number of instances in the data set).Q5: What tree do you get? What is the incorrectly classified error rate?Q6: What are the important variables and their relative importance, according to J48?Q7: Remove the top predictor -- and re-run the J48 using leave-one-out cross validation --what do you get?

Answers

ComputerScienceExpert
(11)
Status NEW Posted 22 May 2017 01:05 AM My Price 8.00

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