02/14/2015,11:55PM on Moodle Please submit your answers in Moodle as one Word file, named HW3_FirstnameLastname.docx, that includes the complete solution to these problems, including your decision tree and final conclusions about the recommended decision. Please also post the corresponding Excel file that contains the decision trees to these problems, one per worksheet, clearly labeled with the problem number. TOTAL: 100 points 1. The NC Airport Authority is trying to solve a difficult problem with the
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Test Toys for 1994 Christmas & Raccoon for Christmas Season Figure 1: Timeline of how Julia buys the toys for the company Decision Problem During last year, three stuffed animals were tested for sale, including a bear, a pig, and a raccoon. The result shown there were 10 out of 50 bears, 4 out of 50 pigs, and 32 out of 50 raccoons sold. Julia was faced with a decision to make about whether she should import the toys from Germany or but the toys domestically, and if the toys were to be imported
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Data Mining Third Edition This page intentionally left blank Data Mining Practical Machine Learning Tools and Techniques Third Edition Ian H. Witten Eibe Frank Mark A. Hall AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann Publishers is an imprint of Elsevier Morgan Kaufmann Publishers is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA This book is printed
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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Classification: Definition Given a collection of records (training set ) – Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen
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incorporated into the post. Update 28-May-2015: Thanks to Dan Steinberg (yes, the CART expert!) for the suggested updates to the CART section which have now been added. 1. C4.5 What does it do? C4.5 constructs a classifier in the form of a decision tree. In order to do this, C4.5 is given a set of data representing things that are already classified. Wait, what’s a classifier? A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts
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investigating the effect of students socio-economic/family background on students academic performance in tertiary institutions using decision tree algorithms A. B. Adeyemo (Ph.D)1 and S. O. Kuyoro (M.Sc.)2 Department of Computer Science, University of Ibadan, Ibadan, Nigeria Abstract The causes of the difference in the academic performance of students in tertiary institutions has for a long time been the focus of study among higher education managers, parents, government and researchers. The cause
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growth rate is greater than the threshold it is categorized as fast growth rate; if the growth rate is less than the threshold it is categorized as slow growth rate. After obtaining the set of categorical attribute growth rates, we build a decision tree on the set. Finally, we characterize the patterns of team success in terms of rules which describe team members’ character attribute growth rates. We present an evaluation of our methodology on three real games: DotA,1 Warcraft III,2 and Starcraft
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DECISION TREE model build STEP 1: build training data set and validation data set. Output of SAS | Meaning of this step | libname homework 'C:\Users\WAN1_XIAO\Downloads';run;data homework.Rabc ; set homework.Abc;IF x10>5.1 then RX10=1;else RX10=0;DROP id x1 x2 x3 x4 x5 x6 x7 x9 x10;run; | RECODE variable for analysis | | Prepare to split data | | Use 70% data to build decision tree model.Use 30% data to build validation data set. | | 1. Based on the question, we can find all
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.........................................................................4 1.2 Data Mining and Decision Trees………………………………………..................….4 1.3 Flow of Report……………………………………….....................................................….5 2. Classification and Regression Trees (CART) …………………………………..........……….6 2.1 Detailed description of CART……………………………………................................6 2.2 Tree Construction………………………………………..............................................….8 2.2.1 Application
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Predicting borrowers’ chance of defaulting on credit loans Junjie Liang (junjie87@stanford.edu) Abstract Credit score prediction is of great interests to banks as the outcome of the prediction algorithm is used to determine if borrowers are likely to default on their loans. This in turn affects whether the loan is approved. In this report I describe an approach to performing credit score prediction using random forests. The dataset was provided by www.kaggle.com, as part of a contest “Give
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