Building And Interpreting Decision Trees in Enterprise Miner.

Slides:

Advertisements

Similar presentations

SAS 9.2 Getting Started. 2006/03/01. SAS Main Window.

Advertisements

Page 1 of 11 Lessees can submit individual Unit Values for their Production Entities if they do not want to utilize the default unit value. If no Unit.

Chapter 7 Classification and Regression Trees

An introduction to data entry, data analysis, and graphing using SPSS

DECISION TREES. Decision trees  One possible representation for hypotheses.

Random Forest Predrag Radenković 3237/10

CPSC 502, Lecture 15Slide 1 Introduction to Artificial Intelligence (AI) Computer Science cpsc502, Lecture 15 Nov, 1, 2011 Slide credit: C. Conati, S.

Huffman code and ID3 Prof. Sin-Min Lee Department of Computer Science.

Data Mining Techniques: Classification. Classification What is Classification? –Classifying tuples in a database –In training set E each tuple consists.

Introduction Training Complexity, Pruning CART vs. ID3 vs. C4.5

Non-Metric Methods: Decision Trees Alexandros Potamianos Dept of ECE, Tech. Univ. of Crete Fall

Logging In Go to web site:

Knowledge Discovery in Databases MIS 637 Professor Mahmoud Daneshmand Fall 2012 Final Project: Red Wine Recipe Data Mining By Jorge Madrazo.

Chapter 7 – Classification and Regression Trees

Chapter 7 – Classification and Regression Trees

Decision Trees IDHairHeightWeightLotionResult SarahBlondeAverageLightNoSunburn DanaBlondeTallAverageYesnone AlexBrownTallAverageYesNone AnnieBlondeShortAverageNoSunburn.

Linear Programming Excel Solver. MAX8X 1 + 5X 2 s.t.2X 1 + 1X 2 ≤ 1000 (Plastic) 3X 1 + 4X 2 ≤ 2400 (Prod. Time) X 1 + X 2 ≤ 700 (Total Prod.) X 1 - X.

Tree-based methods, neutral networks

Decision Trees (2). Numerical attributes Tests in nodes are of the form f i > constant.

Example of a Decision Tree categorical continuous class Splitting Attributes Refund Yes No NO MarSt Single, Divorced Married TaxInc NO < 80K > 80K.

Classification II.

Bill Atwood, Nov. 2002GLAST 1 Classification PSF Analysis A New Analysis Tool: Insightful Miner Classification Trees From Cuts Classification Trees: Recasting.

Classification.

Ensemble Learning (2), Tree and Forest

Decision Tree Models in Data Mining

Beyond Opportunity; Enterprise Miner Ronalda Koster, Data Analyst.

Decision Tree Learning

Lecture Notes 4 Pruning Zhangxi Lin ISQS

Learning what questions to ask. 8/29/03Decision Trees2  Job is to build a tree that represents a series of questions that the classifier will ask of.

Mohammad Ali Keyvanrad

Chapter 9 – Classification and Regression Trees

Zhangxi Lin ISQS Texas Tech University Note: Most slides are from Decision Tree Modeling by SAS Lecture Notes 5 Auxiliary Uses of Trees.

Machine Learning Queens College Lecture 2: Decision Trees.

Summary Statistics Review

XLMiner – a Data Mining Toolkit QuantLink Solutions Pvt. Ltd.

Census Transportation Planning Products CTPP Access Software.

Business Intelligence and Decision Modeling Week 9 Customer Profiling Decision Trees (Part 2) CHAID CRT.

Decision Trees. MS Algorithms Decision Trees The basic idea –creating a series of splits, also called nodes, in the tree. The algorithm adds a node to.

CS 8751 ML & KDDDecision Trees1 Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting.

1 Data Mining dr Iwona Schab Decision Trees. 2 Method of classification Recursive procedure which (progressively) divides sets of n units into groups.

Copyright © 2010 SAS Institute Inc. All rights reserved. Decision Trees Using SAS Sylvain Tremblay SAS Canada – Education SAS Halifax Regional User Group.

MACHINE LEARNING 10 Decision Trees. Motivation  Parametric Estimation  Assume model for class probability or regression  Estimate parameters from all.

1 Decision Tree Learning Original slides by Raymond J. Mooney University of Texas at Austin.

Skill Editor. When you see a Red circle or a next button, like this, that means we want you to click on something. These are the same steps you’ll take.

DECISION TREE Ge Song. Introduction ■ Decision Tree: is a supervised learning algorithm used for classification or regression. ■ Decision Tree Graph:

Decision Tree Learning Presented by Ping Zhang Nov. 26th, 2007.

RetailGraph Date November 30,2012. Version Note Analysis Option in Query Designer. Now you can create analysis report for the dynamic query report.

1 Classification: predicts categorical class labels (discrete or nominal) classifies data (constructs a model) based on the training set and the values.

Classification and Regression Trees

CIS 335 CIS 335 Data Mining Classification Part I.

Outline Decision tree representation ID3 learning algorithm Entropy, Information gain Issues in decision tree learning 2.

1 By: Ashmi Banerjee (125186) Suman Datta ( ) CSE- 3rd year.

Decision Tree Learning DA514 - Lecture Slides 2 Modified and expanded from: E. Alpaydin-ML (chapter 9) T. Mitchell-ML.

DECISION TREES An internal node represents a test on an attribute.

C4.5 algorithm Let the classes be denoted {C1, C2,…, Ck}. There are three possibilities for the content of the set of training samples T in the given node.

C4.5 algorithm Let the classes be denoted {C1, C2,…, Ck}. There are three possibilities for the content of the set of training samples T in the given node.

Ch9: Decision Trees 9.1 Introduction A decision tree:

Introduction to Data Mining and Classification

Advanced Analytics Using Enterprise Miner

Classification and Prediction

Introduction to Data Mining, 2nd Edition by

SAS Homework 2 Review Decision trees

MIS2502: Data Analytics Classification using Decision Trees

Data Mining – Chapter 3 Classification

Directions for Odyssey Test Builder

CSCI N317 Computation for Scientific Applications Unit Weka

Decision Tree Concept of Decision Tree

Classification with CART

STT : Intro. to Statistical Learning

eBilling Training Service Provider Administrator

Presentation transcript:

Building And Interpreting Decision Trees in Enterprise Miner

Getting Up 2 Speed Open up the HMEQ project you worked on last class. You should drop 3 nodes in EM (Input, Insight, and Partition (to separate random training and validation) K:/(common)/tsupra/MARK2042/

Building Decision Trees Add a Tree Node Connect to Data Partition Node

Check Status, Model Role and Measurement

Splitting Criteria: binary target variables default is Ordinal target variables: must use Entropy or Gini. Here, We can use any of the three. These are typical statistical tests. See readings I handed out last class (WebCT).

Close Tree Node. Run it! View the results. Tree with 18 leaves grown based on training data, pruned back to 8 Based on validation. 8-leaf model has accuracy of 89.02% of the Validation set.

Choose View-Tree 10 leaves are visible here. New in EM Version 8.

Tree Options… Follow the tasks below

Colours and Proportion of target value. What did the 0 represent again? Leaves with all zeros will Be green. Individuals who will default on their loan will be Red. Inspect for high percentage of bad loans (red) and good loans (green)

Change the Statistics

Find missing values The branch that contains the Values greater than also Contains the missing values

Select this tab next

View a path to the node Right click an area

Using Tree Options – Default Tree Add New Tree (Default) Make 2 changes to the basic tab. Give This a max and a min set of values 2*25=50 is the RULE. Add the Assessment node And connect.

Close and Save the changes If you didn’t follow the RULE, You won’t be able to save. View the results…

Run it.

View the tree again.

The defaulted tree diagram. Is yours Different?

Running The Assessment Node Run the Assessment Node Select both the Trees

Interpretation View a Lift Chart Results!

Various Charts – what are they saying?

Further Study: See WebCT for more resources. More information on Decision Trees. Assignment 4 also up on WebCT. Group Assignment will be delivered next class.