Decision Trees and other predictive models. Mathias Lanner SAS Institute

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Decision Trees and other predictive models. Mathias Lanner SAS Institute"

Transcription

1 Decision Trees and other predictive models Mathias Lanner SAS Institute

2 Agenda Introduction to Predictive Models Decision Trees Pruning Regression Neural Network Model Assessment 2

3 Predictive Modeling The Essence of Data Mining Most of the big payoff [in data mining] has been in predictive modeling. Herb Edelstein 3

4 Predictive Modeling Applications Database marketing Financial risk management Fraud detection Process monitoring Pattern detection 4

5 Predictive Modeling Training Data Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Numeric or categorical values 5

6 Predictive Modeling Score Data Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Score Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs????? Only input values known 6

7 Predictions Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Predictions Score Data case 1: inputs? case 2: inputs? case 3: inputs? case 4: inputs? case 5: inputs? 7

8 Predictions Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Predictions Score Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs????? 8

9 Predictive Modeling Essentials new case Predict new cases x 3 x 4 Select useful inputs Optimize complexity 9

10 Predictive Modeling Essentials new case Predict new cases x 3 x 4 Select useful inputs Optimize complexity 10

11 Three Prediction Types Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Predictions Decisions Rankings Estimates 11

12 Decision Predictions Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Decisions primary secondary tertiary primary secondary Trained model uses input measurements to make best decision for each case. 12

13 Ranking Predictions Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Rankings Trained model uses input measurements to optimally rank each case. 13

14 Estimate Predictions Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Estimates Trained model uses input measurements to optimally estimate value. 14

15 Model Essentials Predict Review new case Predict new cases Decide, rank, estimate x 3 x 4 Select useful inputs Optimize complexity 15

16 Model Essentials Select Review new case Predict new cases x 3 x 4 Select useful inputs Optimize complexity 16

17 Curse of Dimensionality 1 D 2 D 3 D 17

18 Input Selection Redundancy Irrelevancy 18

19 Model Essentials Select Review new case x 3 x 4 Predict new cases Select useful inputs Optimize complexity Decide, rank, estimate Eradicate redundancies irrelevancies 19

20 Model Essentials Optimize new case Predict new cases x 3 x 4 Select useful inputs Optimize complexity 20

21 Fool s Gold My model fits the training data perfectly... I ve struck it rich! 21

22 Model Complexity Too flexible Not flexible enough 22

23 23 Data Splitting

24 Training Data Role Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Validation Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Training data gives sequence of predictive models with increasing complexity. 24

25 Validation Data Role Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Validation Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Validation data helps select best model from sequence 25

26 Validation Data Role Training Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Validation Data case 1: inputs case 2: inputs case 3: inputs case 4: inputs case 5: inputs Validation data helps select best model from Sequence. 26

27 Model Essentials Optimize new case x 3 x 4 Predict new cases Select useful inputs Optimize complexity Decide, rank, estimate Eradicate redundancies irrelevancies Tune models with validation data 27

28 Agenda Introduction to Predictive Models DECISION TREES Pruning Regression Neural Networks Model Assessment 28

29 Predictive Modeling Tools Primary Decision Tree Regression Neural Network Specialty Dmine Regression MBR AutoNeural Rule Induction DMNeural Multiple Model Ensemble Two Stage 29

30 Predictive Modeling Tools Primary Decision Tree Regression Neural Network Specialty Dmine Regression MBR AutoNeural Rule Induction DMNeural Multiple Model Ensemble Two Stage 30

31 Predictive Modeling Tools Primary Decision Tree Regression Neural Network Specialty Dmine Regression MBR AutoNeural Rule Induction DMNeural Multiple Model Ensemble Two Stage 31

32 Model Essentials Decision Trees new case Predict new cases Prediction rules x 3 x 4 Select useful inputs Split search Optimize complexity Pruning 32

33 Simple Prediction Illustration Analysis goal: Predict the color of a dot based on its location in a scatter plot

34 Model Essentials Decision Trees new case Predict new cases Prediction rules x 3 x 4 Select useful inputs Split search Optimize complexity Pruning 34

35 Decision Tree Prediction Rules 40% leaf node root node < interior node < < % 70% 55%

36 Decision Tree Prediction Rules new case 40% < < < % 70% 55%

37 Decision Tree Prediction Rules new case Decision = Estimate = % < < < % 70% 55%

38 Model Essentials Decision Trees new case Predict new cases Prediction rules x 3 x 4 Select useful inputs Split search Optimize complexity Pruning 38

39 Decision Tree Split Search left right Calculate the logworth of every partition on input

40 Decision Tree Split Search left right 53% 42% 47% 58% max logworth( ) 0.95 Select the partition with maximum logworth

41 Decision Tree Split Search left right 53% 42% 47% 58% Repeat for input. max logworth( )

42 Decision Tree Split Search left right 53% 42% 47% 58% max logworth( ) bottom top % 35% 46% 65% max logworth( )

43 Decision Tree Split Search < Create partition rule from best partition across all inputs

44 Decision Tree Split Search < Repeat process in each subset

45 Decision Tree Split Search left right 61% 40% 39% 60% logworth( ) 5.72 Select the partition with maximum logworth on input

46 Decision Tree Split Search left right 61% 40% 39% 60% bottom top 38% 55% 62% 45% Repeat for input. logworth( ) 5.72 logworth( )

47 Decision Tree Split Search < < Create second partition rule

48 Decision Tree Split Search Repeat to form maximal tree

49 49 Demo Decision Tree

Agenda. Mathias Lanner Sas Institute. Predictive Modeling Applications. Predictive Modeling Training Data. Beslutsträd och andra prediktiva modeller

Agenda. Mathias Lanner Sas Institute. Predictive Modeling Applications. Predictive Modeling Training Data. Beslutsträd och andra prediktiva modeller Agenda Introduktion till Prediktiva modeller Beslutsträd Beslutsträd och andra prediktiva modeller Mathias Lanner Sas Institute Pruning Regressioner Neurala Nätverk Utvärdering av modeller 2 Predictive

More information

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d. EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER ANALYTICS LIFECYCLE Evaluate & Monitor Model Formulate Problem Data Preparation Deploy Model Data Exploration Validate Models

More information

!"!!"#$$%&'()*+$(,%!"#$%$&'()*""%(+,'-*&./#-$&'(-&(0*".$#-$1"(2&."3$'45"

!!!#$$%&'()*+$(,%!#$%$&'()*%(+,'-*&./#-$&'(-&(0*.$#-$1(2&.3$'45 !"!!"#$$%&'()*+$(,%!"#$%$&'()*""%(+,'-*&./#-$&'(-&(0*".$#-$1"(2&."3$'45"!"#"$%&#'()*+',$$-.&#',/"-0%.12'32./4'5,5'6/%&)$).2&'7./&)8'5,5'9/2%.%3%&8':")08';:

More information

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015 An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

More information

Data Mining Classification: Decision Trees

Data Mining Classification: Decision Trees Data Mining Classification: Decision Trees Classification Decision Trees: what they are and how they work Hunt s (TDIDT) algorithm How to select the best split How to handle Inconsistent data Continuous

More information

Enhancing Compliance with Predictive Analytics

Enhancing Compliance with Predictive Analytics Enhancing Compliance with Predictive Analytics FTA 2007 Revenue Estimation and Research Conference Reid Linn Tennessee Department of Revenue reid.linn@state.tn.us Sifting through a Gold Mine of Tax Data

More information

Nine Common Types of Data Mining Techniques Used in Predictive Analytics

Nine Common Types of Data Mining Techniques Used in Predictive Analytics 1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better

More information

Lecture 10: Regression Trees

Lecture 10: Regression Trees Lecture 10: Regression Trees 36-350: Data Mining October 11, 2006 Reading: Textbook, sections 5.2 and 10.5. The next three lectures are going to be about a particular kind of nonlinear predictive model,

More information

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP Csaba Főző csaba.fozo@lloydsbanking.com 15 October 2015 CONTENTS Introduction 04 Random Forest Methodology 06 Transactional Data Mining Project 17 Conclusions

More information

Gerry Hobbs, Department of Statistics, West Virginia University

Gerry Hobbs, Department of Statistics, West Virginia University Decision Trees as a Predictive Modeling Method Gerry Hobbs, Department of Statistics, West Virginia University Abstract Predictive modeling has become an important area of interest in tasks such as credit

More information

Data Mining Techniques Chapter 6: Decision Trees

Data Mining Techniques Chapter 6: Decision Trees Data Mining Techniques Chapter 6: Decision Trees What is a classification decision tree?.......................................... 2 Visualizing decision trees...................................................

More information

Data mining techniques: decision trees

Data mining techniques: decision trees Data mining techniques: decision trees 1/39 Agenda Rule systems Building rule systems vs rule systems Quick reference 2/39 1 Agenda Rule systems Building rule systems vs rule systems Quick reference 3/39

More information

A fast, powerful data mining workbench designed for small to midsize organizations

A fast, powerful data mining workbench designed for small to midsize organizations FACT SHEET SAS Desktop Data Mining for Midsize Business A fast, powerful data mining workbench designed for small to midsize organizations What does SAS Desktop Data Mining for Midsize Business do? Business

More information

Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets

Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets http://info.salford-systems.com/jsm-2015-ctw August 2015 Salford Systems Course Outline Demonstration of two classification

More information

Using multiple models: Bagging, Boosting, Ensembles, Forests

Using multiple models: Bagging, Boosting, Ensembles, Forests Using multiple models: Bagging, Boosting, Ensembles, Forests Bagging Combining predictions from multiple models Different models obtained from bootstrap samples of training data Average predictions or

More information

Data Mining for Knowledge Management. Classification

Data Mining for Knowledge Management. Classification 1 Data Mining for Knowledge Management Classification Themis Palpanas University of Trento http://disi.unitn.eu/~themis Data Mining for Knowledge Management 1 Thanks for slides to: Jiawei Han Eamonn Keogh

More information

Data Mining with R. Decision Trees and Random Forests. Hugh Murrell

Data Mining with R. Decision Trees and Random Forests. Hugh Murrell Data Mining with R Decision Trees and Random Forests Hugh Murrell reference books These slides are based on a book by Graham Williams: Data Mining with Rattle and R, The Art of Excavating Data for Knowledge

More information

Use Data Mining Techniques to Assist Institutions in Achieving Enrollment Goals: A Case Study

Use Data Mining Techniques to Assist Institutions in Achieving Enrollment Goals: A Case Study Use Data Mining Techniques to Assist Institutions in Achieving Enrollment Goals: A Case Study Tongshan Chang The University of California Office of the President CAIR Conference in Pasadena 11/13/2008

More information

Data Mining Using SAS Enterprise Miner Randall Matignon, Piedmont, CA

Data Mining Using SAS Enterprise Miner Randall Matignon, Piedmont, CA Data Mining Using SAS Enterprise Miner Randall Matignon, Piedmont, CA An Overview of SAS Enterprise Miner The following article is in regards to Enterprise Miner v.4.3 that is available in SAS v9.1.3.

More information

TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM

TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM Thanh-Nghi Do College of Information Technology, Cantho University 1 Ly Tu Trong Street, Ninh Kieu District Cantho City, Vietnam

More information

An Overview and Evaluation of Decision Tree Methodology

An Overview and Evaluation of Decision Tree Methodology An Overview and Evaluation of Decision Tree Methodology ASA Quality and Productivity Conference Terri Moore Motorola Austin, TX terri.moore@motorola.com Carole Jesse Cargill, Inc. Wayzata, MN carole_jesse@cargill.com

More information

PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce. Authors: B. Panda, J. S. Herbach, S. Basu, R. J. Bayardo.

PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce. Authors: B. Panda, J. S. Herbach, S. Basu, R. J. Bayardo. PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce Authors: B. Panda, J. S. Herbach, S. Basu, R. J. Bayardo. VLDB 2009 CS 422 Decision Trees: Main Components Find Best Split Choose split

More information

ANALYSIS OF FEATURE SELECTION WITH CLASSFICATION: BREAST CANCER DATASETS

ANALYSIS OF FEATURE SELECTION WITH CLASSFICATION: BREAST CANCER DATASETS ANALYSIS OF FEATURE SELECTION WITH CLASSFICATION: BREAST CANCER DATASETS Abstract D.Lavanya * Department of Computer Science, Sri Padmavathi Mahila University Tirupati, Andhra Pradesh, 517501, India lav_dlr@yahoo.com

More information

ASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS

ASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS DATABASE MARKETING Fall 2015, max 24 credits Dead line 15.10. ASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS PART A Gains chart with excel Prepare a gains chart from the data in \\work\courses\e\27\e20100\ass4b.xls.

More information

MACHINE LEARNING AN INTRODUCTION

MACHINE LEARNING AN INTRODUCTION AN INTRODUCTION JOSEFIN ROSÉN, SENIOR ANALYTICAL EXPERT, SAS INSTITUTE JOSEFIN.ROSEN@SAS.COM TWITTER: @ROSENJOSEFIN AGENDA What is machine learning? When, where and how is machine learning used? Exemple

More information

What is Data Mining? MS4424 Data Mining & Modelling. MS4424 Data Mining & Modelling. MS4424 Data Mining & Modelling. MS4424 Data Mining & Modelling

What is Data Mining? MS4424 Data Mining & Modelling. MS4424 Data Mining & Modelling. MS4424 Data Mining & Modelling. MS4424 Data Mining & Modelling MS4424 Data Mining & Modelling MS4424 Data Mining & Modelling Lecturer : Dr Iris Yeung Room No : P7509 Tel No : 2788 8566 Email : msiris@cityu.edu.hk 1 Aims To introduce the basic concepts of data mining

More information

Overview. Background. Data Mining Analytics for Business Intelligence and Decision Support

Overview. Background. Data Mining Analytics for Business Intelligence and Decision Support Mining Analytics for Business Intelligence and Decision Support Chid Apte, PhD Manager, Abstraction Research Group IBM TJ Watson Research Center apte@us.ibm.com http://www.research.ibm.com/dar Overview

More information

Classification and Prediction

Classification and Prediction Classification and Prediction Slides for Data Mining: Concepts and Techniques Chapter 7 Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser

More information

Neural Networks & Boosting

Neural Networks & Boosting Neural Networks & Boosting Bob Stine Dept of Statistics, School University of Pennsylvania Questions How is logistic regression different from OLS? Logistic mean function for probabilities Larger weight

More information

Data Preprocessing. Week 2

Data Preprocessing. Week 2 Data Preprocessing Week 2 Topics Data Types Data Repositories Data Preprocessing Present homework assignment #1 Team Homework Assignment #2 Read pp. 227 240, pp. 250 250, and pp. 259 263 the text book.

More information

A Property and Casualty Insurance Predictive Modeling Process in SAS

A Property and Casualty Insurance Predictive Modeling Process in SAS Paper 11422-2016 A Property and Casualty Insurance Predictive Modeling Process in SAS Mei Najim, Sedgwick Claim Management Services ABSTRACT Predictive analytics is an area that has been developing rapidly

More information

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Evaluating the Accuracy of a Classifier Holdout, random subsampling, crossvalidation, and the bootstrap are common techniques for

More information

DATA MINING METHODS WITH TREES

DATA MINING METHODS WITH TREES DATA MINING METHODS WITH TREES Marta Žambochová 1. Introduction The contemporary world is characterized by the explosion of an enormous volume of data deposited into databases. Sharp competition contributes

More information

DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES

DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES Vijayalakshmi Mahanra Rao 1, Yashwant Prasad Singh 2 Multimedia University, Cyberjaya, MALAYSIA 1 lakshmi.mahanra@gmail.com

More information

Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal

Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal Learning Example Chapter 18: Learning from Examples 22c:145 An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether

More information

Performance of KDB-Trees with Query-Based Splitting*

Performance of KDB-Trees with Query-Based Splitting* Performance of KDB-Trees with Query-Based Splitting* Yves Lépouchard Ratko Orlandic John L. Pfaltz Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science University of Virginia Illinois

More information

Data Mining Using SAS Enterprise Miner

Data Mining Using SAS Enterprise Miner Data Mining Using SAS Enterprise Miner A Case Study Approach Third Edition SAS Documentation The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2013. Data Mining Using

More information

ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA

ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA D.Lavanya 1 and Dr.K.Usha Rani 2 1 Research Scholar, Department of Computer Science, Sree Padmavathi Mahila Visvavidyalayam, Tirupati, Andhra Pradesh,

More information

Statistical Data Mining. Practical Assignment 3 Discriminant Analysis and Decision Trees

Statistical Data Mining. Practical Assignment 3 Discriminant Analysis and Decision Trees Statistical Data Mining Practical Assignment 3 Discriminant Analysis and Decision Trees In this practical we discuss linear and quadratic discriminant analysis and tree-based classification techniques.

More information

SAS ENTERPRISE MINER 5.3

SAS ENTERPRISE MINER 5.3 FACT SHEET SAS ENTERPRISE MINER 5.3 Unearthing valuable insight profitable data mining results with less time and effort What does SAS Enterprise Miner do? SAS Enterprise Miner streamlines the data mining

More information

Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP

Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP ABSTRACT In data mining modelling, data preparation

More information

Data Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved.

Data Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved. Data Mining with SAS Mathias Lanner mathias.lanner@swe.sas.com Copyright 2010 SAS Institute Inc. All rights reserved. Agenda Data mining Introduction Data mining applications Data mining techniques SEMMA

More information

Predictive Modeling of Titanic Survivors: a Learning Competition

Predictive Modeling of Titanic Survivors: a Learning Competition SAS Analytics Day Predictive Modeling of Titanic Survivors: a Learning Competition Linda Schumacher Problem Introduction On April 15, 1912, the RMS Titanic sank resulting in the loss of 1502 out of 2224

More information

Data Mining Applications in Fund Raising

Data Mining Applications in Fund Raising Data Mining Applications in Fund Raising Nafisseh Heiat Data mining tools make it possible to apply mathematical models to the historical data to manipulate and discover new information. In this study,

More information

Internet Gambling Behavioral Markers: Using the Power of SAS Enterprise Miner 12.1 to Predict High-Risk Internet Gamblers

Internet Gambling Behavioral Markers: Using the Power of SAS Enterprise Miner 12.1 to Predict High-Risk Internet Gamblers Paper 1863-2014 Internet Gambling Behavioral Markers: Using the Power of SAS Enterprise Miner 12.1 to Predict High-Risk Internet Gamblers Sai Vijay Kishore Movva, Vandana Reddy and Dr. Goutam Chakraborty;

More information

Survey Analysis: Data Mining versus Standard Statistical Analysis for Better Analysis of Survey Responses

Survey Analysis: Data Mining versus Standard Statistical Analysis for Better Analysis of Survey Responses Survey Analysis: Data Mining versus Standard Statistical Analysis for Better Analysis of Survey Responses Salford Systems Data Mining 2006 March 27-31 2006 San Diego, CA By Dean Abbott Abbott Analytics

More information

COMP3420: Advanced Databases and Data Mining. Classification and prediction: Introduction and Decision Tree Induction

COMP3420: Advanced Databases and Data Mining. Classification and prediction: Introduction and Decision Tree Induction COMP3420: Advanced Databases and Data Mining Classification and prediction: Introduction and Decision Tree Induction Lecture outline Classification versus prediction Classification A two step process Supervised

More information

ENTERPRISE MINER UNIVERSITY OF AUCKLAND

ENTERPRISE MINER UNIVERSITY OF AUCKLAND SAS ENTERPRISE MINER UNIVERSITY OF AUCKLAND PHILLIP HIGGINS SAS PREDICTIVE ANALYTICS WHY IS IT IMPORTANT? Business Interest Data Growth Technology SAS PREDICTIVE ANALYTICS MOVING FROM REAR VIEW TO FORWARD

More information

CART 6.0 Feature Matrix

CART 6.0 Feature Matrix CART 6.0 Feature Matri Enhanced Descriptive Statistics Full summary statistics Brief summary statistics Stratified summary statistics Charts and histograms Improved User Interface New setup activity window

More information

Start-up Companies Predictive Models Analysis. Boyan Yankov, Kaloyan Haralampiev, Petko Ruskov

Start-up Companies Predictive Models Analysis. Boyan Yankov, Kaloyan Haralampiev, Petko Ruskov Start-up Companies Predictive Models Analysis Boyan Yankov, Kaloyan Haralampiev, Petko Ruskov Abstract: A quantitative research is performed to derive a model for predicting the success of Bulgarian start-up

More information

A Property & Casualty Insurance Predictive Modeling Process in SAS

A Property & Casualty Insurance Predictive Modeling Process in SAS Paper AA-02-2015 A Property & Casualty Insurance Predictive Modeling Process in SAS 1.0 ABSTRACT Mei Najim, Sedgwick Claim Management Services, Chicago, Illinois Predictive analytics has been developing

More information

Data Mining Algorithms Part 1. Dejan Sarka

Data Mining Algorithms Part 1. Dejan Sarka Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses

More information

M15_BERE8380_12_SE_C15.7.qxd 2/21/11 3:59 PM Page 1. 15.7 Analytics and Data Mining 1

M15_BERE8380_12_SE_C15.7.qxd 2/21/11 3:59 PM Page 1. 15.7 Analytics and Data Mining 1 M15_BERE8380_12_SE_C15.7.qxd 2/21/11 3:59 PM Page 1 15.7 Analytics and Data Mining 15.7 Analytics and Data Mining 1 Section 1.5 noted that advances in computing processing during the past 40 years have

More information

Specific Usage of Visual Data Analysis Techniques

Specific Usage of Visual Data Analysis Techniques Specific Usage of Visual Data Analysis Techniques Snezana Savoska 1 and Suzana Loskovska 2 1 Faculty of Administration and Management of Information systems, Partizanska bb, 7000, Bitola, Republic of Macedonia

More information

Data Mining. Nonlinear Classification

Data Mining. Nonlinear Classification Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15

More information

Welcome. Data Mining: Updates in Technologies. Xindong Wu. Colorado School of Mines Golden, Colorado 80401, USA

Welcome. Data Mining: Updates in Technologies. Xindong Wu. Colorado School of Mines Golden, Colorado 80401, USA Welcome Xindong Wu Data Mining: Updates in Technologies Dept of Math and Computer Science Colorado School of Mines Golden, Colorado 80401, USA Email: xwu@ mines.edu Home Page: http://kais.mines.edu/~xwu/

More information

A Comparison of Decision Tree and Logistic Regression Model Xianzhe Chen, North Dakota State University, Fargo, ND

A Comparison of Decision Tree and Logistic Regression Model Xianzhe Chen, North Dakota State University, Fargo, ND Paper D02-2009 A Comparison of Decision Tree and Logistic Regression Model Xianzhe Chen, North Dakota State University, Fargo, ND ABSTRACT This paper applies a decision tree model and logistic regression

More information

Clustering UE 141 Spring 2013

Clustering UE 141 Spring 2013 Clustering UE 141 Spring 013 Jing Gao SUNY Buffalo 1 Definition of Clustering Finding groups of obects such that the obects in a group will be similar (or related) to one another and different from (or

More information

Supervised Learning (Big Data Analytics)

Supervised Learning (Big Data Analytics) Supervised Learning (Big Data Analytics) Vibhav Gogate Department of Computer Science The University of Texas at Dallas Practical advice Goal of Big Data Analytics Uncover patterns in Data. Can be used

More information

Smart Grid Data Analytics for Decision Support

Smart Grid Data Analytics for Decision Support 1 Smart Grid Data Analytics for Decision Support Prakash Ranganathan, Department of Electrical Engineering, University of North Dakota, Grand Forks, ND, USA Prakash.Ranganathan@engr.und.edu, 701-777-4431

More information

Unit 4 DECISION ANALYSIS. Lesson 37. Decision Theory and Decision Trees. Learning objectives:

Unit 4 DECISION ANALYSIS. Lesson 37. Decision Theory and Decision Trees. Learning objectives: Unit 4 DECISION ANALYSIS Lesson 37 Learning objectives: To learn how to use decision trees. To structure complex decision making problems. To analyze the above problems. To find out limitations & advantages

More information

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery Index Contents Page No. 1. Introduction 1 1.1 Related Research 2 1.2 Objective of Research Work 3 1.3 Why Data Mining is Important 3 1.4 Research Methodology 4 1.5 Research Hypothesis 4 1.6 Scope 5 2.

More information

Smart Sell Re-quote project for an Insurance company.

Smart Sell Re-quote project for an Insurance company. SAS Analytics Day Smart Sell Re-quote project for an Insurance company. A project by Ajay Guyyala Naga Sudhir Lanka Narendra Babu Merla Kiran Reddy Samiullah Bramhanapalli Shaik Business Situation XYZ

More information

Better credit models benefit us all

Better credit models benefit us all Better credit models benefit us all Agenda Credit Scoring - Overview Random Forest - Overview Random Forest outperform logistic regression for credit scoring out of the box Interaction term hypothesis

More information

How Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK

How Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK How Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK Agenda Analytics why now? The process around data and text mining Case Studies The Value of Information

More information

Data Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland

Data Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland Data Mining and Knowledge Discovery in Databases (KDD) State of the Art Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland 1 Conference overview 1. Overview of KDD and data mining 2. Data

More information

Clustering. Data Mining. Abraham Otero. Data Mining. Agenda

Clustering. Data Mining. Abraham Otero. Data Mining. Agenda Clustering 1/46 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference 2/46 1 Introduction It seems logical that in a new situation we should act in a similar way as in

More information

Evaluation of Feature Selection Methods for Predictive Modeling Using Neural Networks in Credits Scoring

Evaluation of Feature Selection Methods for Predictive Modeling Using Neural Networks in Credits Scoring 714 Evaluation of Feature election Methods for Predictive Modeling Using Neural Networks in Credits coring Raghavendra B. K. Dr. M.G.R. Educational and Research Institute, Chennai-95 Email: raghavendra_bk@rediffmail.com

More information

Classification algorithm in Data mining: An Overview

Classification algorithm in Data mining: An Overview Classification algorithm in Data mining: An Overview S.Neelamegam #1, Dr.E.Ramaraj *2 #1 M.phil Scholar, Department of Computer Science and Engineering, Alagappa University, Karaikudi. *2 Professor, Department

More information

Data Mining Part 5. Prediction

Data Mining Part 5. Prediction Data Mining Part 5. Prediction 5.7 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Linear Regression Other Regression Models References Introduction Introduction Numerical prediction is

More information

Data Mining. 1 Introduction 2 Data Mining methods. Alfred Holl Data Mining 1

Data Mining. 1 Introduction 2 Data Mining methods. Alfred Holl Data Mining 1 Data Mining 1 Introduction 2 Data Mining methods Alfred Holl Data Mining 1 1 Introduction 1.1 Motivation 1.2 Goals and problems 1.3 Definitions 1.4 Roots 1.5 Data Mining process 1.6 Epistemological constraints

More information

Data Mining Part 5. Prediction

Data Mining Part 5. Prediction Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification

More information

Interactive Data Mining and Design of Experiments: the JMP Partition and Custom Design Platforms

Interactive Data Mining and Design of Experiments: the JMP Partition and Custom Design Platforms : the JMP Partition and Custom Design Platforms Marie Gaudard, Ph. D., Philip Ramsey, Ph. D., Mia Stephens, MS North Haven Group March 2006 Table of Contents Abstract... 1 1. Data Mining... 1 1.1. What

More information

Predicting Student Performance by Using Data Mining Methods for Classification

Predicting Student Performance by Using Data Mining Methods for Classification BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 13, No 1 Sofia 2013 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.2478/cait-2013-0006 Predicting Student Performance

More information

Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

More information

Data Mining: Overview. What is Data Mining?

Data Mining: Overview. What is Data Mining? Data Mining: Overview What is Data Mining? Recently * coined term for confluence of ideas from statistics and computer science (machine learning and database methods) applied to large databases in science,

More information

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4.

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4. Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví Pavel Kříž Seminář z aktuárských věd MFF 4. dubna 2014 Summary 1. Application areas of Insurance Analytics 2. Insurance Analytics

More information

Data Mining. SPSS Clementine 12.0. 1. Clementine Overview. Spring 2010 Instructor: Dr. Masoud Yaghini. Clementine

Data Mining. SPSS Clementine 12.0. 1. Clementine Overview. Spring 2010 Instructor: Dr. Masoud Yaghini. Clementine Data Mining SPSS 12.0 1. Overview Spring 2010 Instructor: Dr. Masoud Yaghini Introduction Types of Models Interface Projects References Outline Introduction Introduction Three of the common data mining

More information

THE HYBRID CART-LOGIT MODEL IN CLASSIFICATION AND DATA MINING. Dan Steinberg and N. Scott Cardell

THE HYBRID CART-LOGIT MODEL IN CLASSIFICATION AND DATA MINING. Dan Steinberg and N. Scott Cardell THE HYBID CAT-LOGIT MODEL IN CLASSIFICATION AND DATA MINING Introduction Dan Steinberg and N. Scott Cardell Most data-mining projects involve classification problems assigning objects to classes whether

More information

Classification/Decision Trees (II)

Classification/Decision Trees (II) Classification/Decision Trees (II) Department of Statistics The Pennsylvania State University Email: jiali@stat.psu.edu Right Sized Trees Let the expected misclassification rate of a tree T be R (T ).

More information

Decision Trees What Are They?

Decision Trees What Are They? Decision Trees What Are They? Introduction...1 Using Decision Trees with Other Modeling Approaches...5 Why Are Decision Trees So Useful?...8 Level of Measurement... 11 Introduction Decision trees are a

More information

A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINE

A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINE A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINE Kasra Madadipouya 1 1 Department of Computing and Science, Asia Pacific University of Technology & Innovation ABSTRACT Today, enormous amount of data

More information

Didacticiel Études de cas

Didacticiel Études de cas 1 Theme Data Mining with R The rattle package. R (http://www.r project.org/) is one of the most exciting free data mining software projects of these last years. Its popularity is completely justified (see

More information

PharmaSUG2011 Paper HS03

PharmaSUG2011 Paper HS03 PharmaSUG2011 Paper HS03 Using SAS Predictive Modeling to Investigate the Asthma s Patient Future Hospitalization Risk Yehia H. Khalil, University of Louisville, Louisville, KY, US ABSTRACT The focus of

More information

. g .,, . . , Applicability of

More information

Russell K. Anderson. Visual Data Mining THE VISMINER APPROACH

Russell K. Anderson. Visual Data Mining THE VISMINER APPROACH Russell K. Anderson Visual Data Mining THE VISMINER APPROACH Visual Data Mining Visual Data Mining The VisMiner Approach RUSSELL K. ANDERSON VisTech, USA This edition first published 2013 # 2013 John

More information

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP Data Warehousing and End-User Access Tools OLAP and Data Mining Accompanying growth in data warehouses is increasing demands for more powerful access tools providing advanced analytical capabilities. Key

More information

Feature vs. Classifier Fusion for Predictive Data Mining a Case Study in Pesticide Classification

Feature vs. Classifier Fusion for Predictive Data Mining a Case Study in Pesticide Classification Feature vs. Classifier Fusion for Predictive Data Mining a Case Study in Pesticide Classification Henrik Boström School of Humanities and Informatics University of Skövde P.O. Box 408, SE-541 28 Skövde

More information

Why Ensembles Win Data Mining Competitions

Why Ensembles Win Data Mining Competitions Why Ensembles Win Data Mining Competitions A Predictive Analytics Center of Excellence (PACE) Tech Talk November 14, 2012 Dean Abbott Abbott Analytics, Inc. Blog: http://abbottanalytics.blogspot.com URL:

More information

Applying Data Mining Techniques Using SAS Enterprise Miner. Course Notes

Applying Data Mining Techniques Using SAS Enterprise Miner. Course Notes Applying Data Mining Techniques Using SAS Enterprise Miner Course Notes Applying Data Mining Techniques Using SAS Enterprise Miner Course Notes was developed by Sue Walsh. Some of the course notes is based

More information

SELF-ORGANISING MAPPING NETWORKS (SOM) WITH SAS E-MINER

SELF-ORGANISING MAPPING NETWORKS (SOM) WITH SAS E-MINER SELF-ORGANISING MAPPING NETWORKS (SOM) WITH SAS E-MINER C.Sarada, K.Alivelu and Lakshmi Prayaga Directorate of Oilseeds Research, Rajendranagar, Hyderabad saradac@yahoo.com Self Organising mapping networks

More information

Knowledge Discovery and Data Mining. Structured vs. Non-Structured Data

Knowledge Discovery and Data Mining. Structured vs. Non-Structured Data Knowledge Discovery and Data Mining Unit # 2 1 Structured vs. Non-Structured Data Most business databases contain structured data consisting of well-defined fields with numeric or alphanumeric values.

More information

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Introduction to Data Mining

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Introduction to Data Mining 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

More information

Data Mining Approaches to Collections and Case Closure. Background

Data Mining Approaches to Collections and Case Closure. Background Data Mining Approaches to Collections and Case Closure Bill Haffey Technical Director, SPSS Public Sector Background Florida DOR has 500,000 sales accounts, of which ~35,000 are likely to be in the collections

More information

Full and Complete Binary Trees

Full and Complete Binary Trees Full and Complete Binary Trees Binary Tree Theorems 1 Here are two important types of binary trees. Note that the definitions, while similar, are logically independent. Definition: a binary tree T is full

More information

Application of Data Mining Techniques in Improving Breast Cancer Diagnosis

Application of Data Mining Techniques in Improving Breast Cancer Diagnosis Application of Data Mining Techniques in Improving Breast Cancer Diagnosis ABSTRACT Breast cancer is the second leading cause of cancer deaths among women in the United States. Although mortality rates

More information

Chapter 12 Discovering New Knowledge Data Mining

Chapter 12 Discovering New Knowledge Data Mining Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to

More information

Clustering. Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016

Clustering. Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016 Clustering Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016 1 Supervised learning vs. unsupervised learning Supervised learning: discover patterns in the data that relate data attributes with

More information

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Combining multiple models Bagging The basic idea

More information

Data Mining Lab 5: Introduction to Neural Networks

Data Mining Lab 5: Introduction to Neural Networks Data Mining Lab 5: Introduction to Neural Networks 1 Introduction In this lab we are going to have a look at some very basic neural networks on a new data set which relates various covariates about cheese

More information