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IBM SPSS Modeler Cookbook

By : Keith McCormick, Abbott

4.4 (20)

IBM SPSS Modeler Cookbook

4.4 (20)

By: Keith McCormick, Abbott

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.

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. 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. 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. 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. 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. 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. 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. 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. 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

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

Index

Index

Customer Reviews

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1 star

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What is CRISP-DM?

CRISP-DM is a tool that is a neutral and industry-nonspecific process model for navigating a data mining project life cycle. It consists of six phases, and within those phases, a total of 24 generic tasks. In the given table, one can see the phases as column headings, and the generic tasks in bold. It is the most widely used process model of its kind. This is especially true of users of Modeler since the software has historically made explicit references to CRISP-DM in the default structure of the project files, but the polls have shown that its popularity extends to many data miners. It was written in the 90s by a consortium of data miners from numerous companies. Its lead authors were from NCR, Daimler Chrysler, and ISL (later bought by SPSS).

What is CRISP-DM?

This book uses this process model to structure the book but does not address the CRISP-DM content directly. Since the CRISP-DM consortium is nonprofit, the original documents are widely available on the Web, and it would be helpful to read it entirely as part of one's data mining professional development. Naturally, as a cookbook written for users of Modeler, our focus will be on hands-on tasks.

Business understanding, while critical, is not conducive to a recipe-based format. It is such an important topic, which is why it is covered in Appendix, Business Understanding, in prose. Data preparation receives the most of our attention with four chapters. Modeling is covered, in depth, in its own chapter. Since evaluation and deployment often use Modeler in combination with other tools, we have included them in somewhat fewer recipes, but that does not diminish its importance. The final chapter, Modeler Scripting, is not named after a CRISP-DM phase or a task but is included at the end because it has the most advanced recipes.

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