Assignment06.pdf - EAPS 507 Assignment 06 Logistic Regression for Classification Your Name Here Instructions Important 1 Please change yourUsername to

Assignment06.pdf - EAPS 507 Assignment 06 Logistic...

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EAPS 507 Assignment 06 Logistic Regression for Classification Your Name Here 10/04/2019 Instructions: Important 1. Please change yourUsername to your Purdue Career Account name; that is, your email without @pur- due.edu 2. Save the file with this new filename with the .Rmd (R Markdown) extension 3. Answer the questions below. 4. The minimum check for correctness is to run all chunks of code without Error messages. Due Dates Knit your file into a pdf or a Word document to upload in Circuit by 11:59AM on 10/16/2019 Peer-Review by 11:59AM on 10/18 on Circuit Final R Markdown (Rmd) file due to Blackboard by 11:59AM on 10/19 (Saturday) Package: We will use the data Sonar in package mlbench . You need to have the package installed library (mlbench) 1. Introduction (Taken from ISLR Chapter 4) The linear regression model discussed in Chapter 3 assumes that the response variable Y is quantitative (numerical). But in many situations, the response variable is instead qualitative (categorical). For example, eye color is qualitative, taking on values blue, brown, or green. Predicting a qualitative response for an observation can be referred to as classifying that observation, since it involves assigning the observation to a category, or class. Often the methods used for classification first predict the probability of each of the categories of a qualitative variable, as the basis for making the classi- fication. In this sense they also behave like regression methods. There are many possible classification techniques, or classifiers, that one might use to predict a qualitative response. Logistic regression is among the most widely-used classifiers. Review IV. Hands-on Exercise: 3.Video:Lab:Logistic Regression in Course BBSite’s Chapter 7. Study Ch. 4.6.2 in ISLR . The video and text use Stock Market Data to demonstrate logistic regression; you will answer following questions using Sonar data to distinguish rocks from mines. 1
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1. Distinguish rocks from mines from sonar data The data have been taken from the UCI Repository of Machine Learning Databases. Reference: Gorman, R. P., and Sejnowski, T. J. (1988). “Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets”, Neural Networks , Vol. 1 , pp. 75-89. This is the data set in the above study of the classification of sonar signals using a neural network. The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
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