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

5 star

4 star

3 star

2 star

1 star

Quantifying variable importance with Monte Carlo simulation

Finding the smallest subset of all possible input variables that result in an accurate model (that is, a parsimonious solution) is often the biggest challenge for many data mining projects. It's common for data sets to contain 10s to 100s of input variables. Models that are over-trained or simply fail to build are both possible with so called "wide" data sets. Removing unimportant variables to find the sweet spot between model accuracy and stability is where experienced data miners can deliver significant value.

The primary method of variable selection in Modeler is Feature Selection. The Feature Selection process identifies the significance of each variable individually. Statistically insignificant variables below a specified p-value are dropped. While this technique works well with simple data sets and "main effects" models such as regression, it completely ignores the interaction between variables. As often happens, the interaction...

IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

Overview of this book

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