Book Image

IBM SPSS Modeler Cookbook

Book Image

IBM SPSS Modeler Cookbook

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.

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IBM SPSS Modeler Cookbook
Oct, 2013 | 382 pages
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Table of Contents (11 chapters)
Preface
Preface
What is CRISP-DM?
Data mining is a business process
The IBM SPSS Modeler workbench
Historical introduction to scripting
What this book covers
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Data Understanding
1. Data Understanding
Introduction
Using an empty aggregate to evaluate sample size
Evaluating the need to sample from the initial data
Using CHAID stumps when interviewing an SME
Using a single cluster K-means as an alternative to anomaly detection
Using an @NULL multiple Derive to explore missing data
Creating an Outlier report to give to SMEs
Detecting potential model instability early using the Partition node and Feature Selection node
2
2. Data Preparation – Select
2. Data Preparation – Select
Introduction
Using the Feature Selection node creatively to remove or decapitate perfect predictors
Running a Statistics node on anti-join to evaluate the potential missing data
Evaluating the use of sampling for speed
Removing redundant variables using correlation matrices
Selecting variables using the CHAID Modeling node
Selecting variables using the Means node
Selecting variables using single-antecedent Association Rules
3
3. Data Preparation – Clean
3. Data Preparation – Clean
Introduction
Binning scale variables to address missing data
Using a full data model/partial data model approach to address missing data
Imputing in-stream mean or median
Imputing missing values randomly from uniform or normal distributions
Using random imputation to match a variable's distribution
Searching for similar records using a Neural Network for inexact matching
Using neuro-fuzzy searching to find similar names
Producing longer Soundex codes
4
4. Data Preparation – Construct
4. Data Preparation – Construct
Introduction
Building transformations with multiple Derive nodes
Calculating and comparing conversion rates
Grouping categorical values
Transforming high skew and kurtosis variables with a multiple Derive node
Creating flag variables for aggregation
Using Association Rules for interaction detection/feature creation
Creating time-aligned cohorts
5
5. Data Preparation – Integrate and Format
5. Data Preparation – Integrate and Format
Introduction
Speeding up merge with caching and optimization settings
Merging a lookup table
Shuffle-down (nonstandard aggregation)
Cartesian product merge using key-less merge by key
Multiplying out using Cartesian product merge, user source, and derive dummy
Changing large numbers of variable names without scripting
Parsing nonstandard dates
Parsing and performing a conversion on a complex stream
Sequence processing
6
6. Selecting and Building a Model
6. Selecting and Building a Model
Introduction
Evaluating balancing with Auto Classifier
Building models with and without outliers
Using Neural Network for Feature Selection
Creating a bootstrap sample
Creating bagged logistic regression models
Using KNN to match similar cases
Using Auto Classifier to tune models
Next-Best-Offer for large datasets
7
7. Modeling – Assessment, Evaluation, Deployment, and Monitoring
7. Modeling – Assessment, Evaluation, Deployment, and Monitoring
Introduction
How (and why) to validate as well as test
Using classification trees to explore the predictions of a Neural Network
Correcting a confusion matrix for an imbalanced target variable by incorporating priors
Using aggregate to write cluster centers to Excel for conditional formatting
Creating a classification tree financial summary using aggregate and an Excel Export node
Reformatting data for reporting with a Transpose node
Changing formatting of fields in a Table node
Combining generated filters
8
8. CLEM Scripting
8. CLEM Scripting
Introduction
Building iterative Neural Network forecasts
Quantifying variable importance with Monte Carlo simulation
Implementing champion/challenger model management
Detecting outliers with the jackknife method
Optimizing K-means cluster solutions
Automating time series forecasts
Automating HTML reports and graphs
Rolling your own modeling algorithm – Weibull analysis
9
A. Business Understanding
A. Business Understanding
Introduction
Define business objectives by Tom Khabaza
Assessing the situation by Meta Brown
Translating your business objective into a data mining objective by Dean Abbott
Produce a project plan – ensuring a realistic timeline by Keith McCormick
10
Index
Index

Building transformations with multiple Derive nodes

In this recipe we will create several new variables with a single Derive node by invoking the multiple radio button.

Getting ready

This recipe uses the datafile cup98lrn_reduced_vars3.sav and the stream Recipe - variable construct multiple derive.str.

How to do it...

  1. Open the stream Recipe - variable construct multiple derive.str by clicking on File | Open Stream.

  2. Make sure the datafile points to the correct path to the datafile cup98lrn_reduced_vars3.sav.

  3. Add a Derive node to the stream and connect it to the Type node called First Type.

  4. Open the Derive node, click on the Multiple radio button. In the Derive from region, select all of the RFA variables, from RFA_2 to RFA_24. You can select multiple variables by clicking on RFA_2, scrolling down to RFA_24 and Shift-clicking on RFA_24. Then click on OK.

  5. In the Field name extension box, change the text _Derive to R.

  6. In the Field Type drop-down list, select Nominal. This is shown in the following screenshot...

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