IBM SPSS Modeler Cookbook

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.

IBM SPSS Modeler Cookbook

Credits

Foreword

About the Authors

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

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

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

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

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

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

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

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

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

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

Index

Customer Reviews

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Using CHAID stumps when interviewing an SME

In this recipe we will learn how to use the interactive mode of the CHAID Modeling node to explore data. The name stump comes from the idea that we grow just one branch and stop. The exploration will have the goal of answering five questions:

What variables seem predictive of the target?
Do the most predictive variables make sense?
What questions are most useful to pose to the Subject Matter Experts (SMEs) about data quality?
What is the potential value of the favorite variables of the SMEs?
What missing data challenges are present in the data?

Getting ready

We will start with a blank stream.

How to do it...

To use CHAID stumps:

Add a Source node to the stream for the cup98lrn reduced vars2.txt file. Ensure that the field delimiter is Tab and that the Strip lead and trail spaces option is set to Both.
Add a Type node and declare TARGET_B as flag and as the target. Set TARGET_D, RFA_2, RFA_2A, and RFA_2F, RFA_2R to None.
Add a CHAID Modeling node and make sure that it is in interactive mode.
Click on the Run button. From the menus choose Tree | Grow Branch with Custom Split. Then click on the Predictors button.
Allow the top variable, LASTGIFT, to form a branch. Note that LASTGIFT does not seem to have missing values.
Further down the list, the RAMNT_ series variables do have missing values. Placing the mouse on the root node (Node 0) choose Tree | Grow Branch with Custom Split again.
The figure shows RAMNT_8, but your results may differ somewhat as CHAIDtakes an internal partition and therefore does not use all of the data. The slight differences can change the ranking of similar variables. Allow the branch to grow on your selected variable.
Now we will break away the missing data into its own category. Repeat the steps leading up to this branch, but before clicking on the Grow button, select Custom and at the bottom, set Missing values into as Separate Node.
Sometimes SMEs will have a particular interest in a variable because it has been known to be valuable in the past, or they are invested in the variable in some way. Even though it is well down the list, choose the variable Wealth2 and force it to branch while ensuring that missing values are placed into a Separate node.

How it works...

There are several advantages to exploring data in this way with CHAID. If you have accidentally included perfect predictors it will become obvious in a hurry. This recipe is dedicated to this phenomenon. Another advantage is that most SMEs find CHAID rather intuitive. It is easy to see what the relationships are without extensive exposure to the technique. Meanwhile, as an added benefit, the SMEs are becoming acquainted with a technique that might also be used during the modeling phase. As we have seen, CHAID can show missing data as a Separate node. This feature is shown to be useful in the Binning scale variables to address missing data recipe in Chapter 3, Data Preparation – Clean. By staying in interactive mode, the trees are kept simple; also, we can force any variable to branch even if it is not near the top of the list. Often SMEs can be quite adamant that a variable is important, while the data shows them otherwise. There are countless reasons why this might be the case, and the conversation should be allowed to unfold. One is likely to learn a great deal trying to figure out why a variable that seemed promising is not performing well in the CHAID model.

Let's examine the CHAID tree a bit more closely. The root node shows the total sample size and the percentage in each of the two categories. In the figures in this recipe, the red group is the donors group. Notice that the more recent their LASTGIFT was, the more likely that they donated. Starting with 8.286 percent for the less than or equal to 9 group, dropping down to 3.476 percent for the less than 19 group. Note that when you add up the child nodes, you get the same number as the number in the root node.

It is recommended that you take a screenshot of at least the top 10 or so variables of interest to management or SMEs. It is a good precaution to place the images on slides, since you will be able to review and discuss without waiting for Modeler to process. Having said that, it is an excellent idea to be ready to further explore the data using this technique on live data during the meeting.

IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

Overview of this book

Related Content you might be interested in

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IBM SPSS Modeler Cookbook

Using CHAID stumps when interviewing an SME

Getting ready

How to do it...

How it works...

See also