Decision Trees 17.0
Create highly visual decision trees directly within SPSS Statistics to help you better identify groups, discover relationships between groups, and predict future events. Learn more about the SPSS Decision Trees add-on module (formerly called SPSS Classification Trees).
Easily Identify Groups and Predict Outcomes
The SPSS Decision Trees* add-on module creates classification and decision trees directly within SPSS Statistics Base to help you better identify groups,
discover relationships between groups, and predict future events. You can use classification and decision trees for segmentation, stratification, prediction, data reduction and variable screening, interaction identification, category merging, and discretizing continuous variables. Highly visual diagrams enable you to present categorical results in an intuitive manner—so you can more clearly explain categorical results to non-technical audiences. These trees enable you to explore your results and visually determine how your model flows. Visual results can help you find specific subgroups and relationships that you might not uncover using more traditional statistics. Because classification trees break the data down into branches and nodes, you can easily see where a group splits and terminates.
Use SPSS Decision Trees in a variety of applications, including:
– Choose a response variable to segment your customer base (for example, responders/non-responders in a test mailing; high-, medium-, and low-profit customers; or recruits who have extended service versus those who haven’t)
– Profile groups based on other attributes, such as demographics or customer activity
– Customize new promotions to focus on a specific subgroup, help reduce costs, and improve return on investment (ROI)
Market research
– Perform customer, employee, or recruit satisfaction surveys
– Choose a variable that measures satisfactio (for example, o a “1-5” scale)
– Profile satisfaction levels according to responses to other questions
– Chage factors, such as work environment product quality, that can affect satisfaction
Credit risk scoring
– Determine risk groups (high, medium, or low)
– Profile risk groups based on customer information, such as account activity
– Offer the right credit line to the right applicants based on risk group
Program targeting
– Choose a variable with a desirable versus undesirable outcome (for example, successful completion of a welfare-to-work program)
– Reveal the factors that lead to success, based on applicant information
– Customize new programs to satisfy the needs of more people
Marketing in the public sector
– Choose a response variable for segmenting your customer base (for example, potential college applicants who actually applied versus those who haven’t)
– Profile groups based on other attributes, such as demographics or customer activity
– Customize new promotions to focus on a specific subgroup, help reduce costs, and improve ROI
Choose from four decision tree algorithms
SPSS Decision Trees includes four established tree-growing algorithms:
- CHAID — A fast, statistical, multi-way tree algorithm that explores data quickly and efficiently, and builds segments and profiles with respect to the desired
outcome
- Exhaustive CHAID — A modification of CHAID that examines all possible splits for each predictor
- Classification & regression trees (C&RT) — A complete binary tree algorithm that partitions data and produces accurate homogeneous subsets
- QUEST — A statistical algorithm that selects variables without bias and builds accurate binary trees quickly and efficiently
With four algorithms, you have the ability to try different tree-growing methods and find the one that best fits your data.
Extend your results with further analysis within SPSS
Since you use SPSS Decision Trees within the SPSS Statistics Base interface, you can easily create classification trees and conveniently use the results to
segment and group cases directly within the data. There is no back and forth between SPSS Statistics Base and other software. Additionally, you can generate selection or classification/prediction rules in the form of SPSS Statistics syntax, SQL statements, or simple text (through syntax). You can display these rules in the Viewer and save them to an external file for later use to make predictions about individual and new cases. If you’d like to use your results
to score other data files, you can write information from the tree model directly to your data or create XML models for use in SPSS Statistics Base Server.
System requirements
For SPSS Statistics Base 17.0 for Windows
Operating System
MicrosoftWindows XP (32-bit versions) or Vista(32-bit or 64-bit versions)
Hardware
Intelor AMD x86 processor running at 1GHz or higher
Memory: 512MB RAM; 1GB recommended
Minimum free drive space: 450MB
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
For connecting with SPSS Statistics Base Server, a network adapter running the TCP/IP network protocol
Software
Web browser: Internet Explorer 6 or above
For SPSS Statistics Base 17.0 for Mac OS X
Operating system: Apple Mac OS X 10.4 (Tiger) or Mac OSX 10.5 (Leopard)
Hardware
PowerPC or Intel processor
Memory: 512MB RAM; 1GB recommended
Minimum free drive space: 800MB
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
Software
Safari 1.3.1, MozillaFirefox1.5 or higher, or Netscape7.2 or higher
Java Standard Edition 5.0 (J2SE 5.0)
SPSS Statistics Base 17.0 for Linux
Operating system*
Any Linux OS that meets the following requirements:
Kernel 2.6.9.42 or higher
glibc 2.3.4 or higher
XFree86-4.0 or higher
libstdc++5
Hardware
Processor: Intel or AMD x86 processor running at 1 GHz or higher
RAM: 512MB RAM; 1GB recommended
450 MB of available hard-disk space
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
Software
Web browser: Konqueror 3.4.1 or higher, or Firefox 1.0.6 or higher, or Netscape 7.2 or higher
*Note: SPSS Statistics 17.0 was tested on and is supported only on Red HatEnterprise Linux 4 Desktop and Debian4.0
SPSS Statistics add-on modules
All SPSS Statistics 17.0 add-on modules require SPSS Statistics Base 17.0.
No other system requirements are necessary.
Amos 17.0
Operating system: Windows XP or Windows Vista
Hardware:
Memory: 256MB RAM minimum
125MB or more available hard-drive space
Web browser: Internet Explorer 6.0
SPSS Statistics Server 17.0
Operating system: Windows Server 2003 or Windows Server 2008 (32-bit or 64-bit) or Windows Server 2008 (32-bit or 64-bit); Sun Solaris (SPARC) 9 and later (64-bit only); IBMAIX5.3 and later; or Red Hat Enterprise Linux ES4 and later (64-bit); HP-UX 11i (64-bit Itanium)
Hardware
Minimum CPU: Two CPUs recommended, running at 1GHz or higher
Memory: 512MB RAM per expected concurrent user
Minimum free drive space: 300MB
Required temporary disk space: Calculate by multiplying 2.5 x number of users x expected size of dataset in megabytes
SPSS Statistics Adapter for SPSS Predictive Enterprise Services
Requires Statistics Base 17.0 and SPSS Predictive Enterprise Services
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