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Index
IBM SPSS Modeler Cookbook
Table of Contents IBM SPSS Modeler Cookbook Credits Foreword About the Authors About the Reviewers www.PacktPub.com
Support files, eBooks, discount offers, and more
Why Subscribe? Free Access for Packt account holders Instant Updates on New Packt Books
Preface
What is CRISP-DM? Data mining is a business process The IBM SPSS Modeler workbench
A brief history of the Clementine workbench
Historical introduction to scripting What this book covers Who this book is for Conventions Reader feedback Customer support
Downloading the example code Errata Piracy Questions
1. Data Understanding
Introduction Using an empty aggregate to evaluate sample size
Getting ready How to do it... How it works... There's more...
A modified version
See also
Evaluating the need to sample from the initial data
Getting ready How to do it... How it works... There's more... See also
Using CHAID stumps when interviewing an SME
Getting ready How to do it... How it works... See also
Using a single cluster K-means as an alternative to anomaly detection
Getting ready How to do it... How it works... There's more...
Using an @NULL multiple Derive to explore missing data
Getting ready How to do it... How it works... See also
Creating an Outlier report to give to SMEs
Getting ready How to do it... How it works... See also
Detecting potential model instability early using the Partition node and Feature Selection node
Getting ready How to do it... How it works... See also
2. Data Preparation – Select
Introduction Using the Feature Selection node creatively to remove or decapitate perfect predictors
Getting ready How to do it... How it works... There's more... See also
Running a Statistics node on anti-join to evaluate the potential missing data
Getting ready How to do it... How it works... See also
Evaluating the use of sampling for speed
Getting ready How to do it... How it works... There's more... See also
Removing redundant variables using correlation matrices
Getting ready How to do it... How it works... There's more... See also
Selecting variables using the CHAID Modeling node
Getting ready How to do it... How it works... There's more... See also
Selecting variables using the Means node
Getting ready How to do it... How it works... There's more... See also
Selecting variables using single-antecedent Association Rules
Getting ready How to do it... How it works... There's more... See also
3. Data Preparation – Clean
Introduction Binning scale variables to address missing data
Getting ready How to do it... How it works... See also
Using a full data model/partial data model approach to address missing data
Getting ready How to do it... How it works... There's more... See also
Imputing in-stream mean or median
Getting ready How to do it... How it works... There's more... See also
Imputing missing values randomly from uniform or normal distributions
Getting ready How to do it... How it works... There's more... See also
Using random imputation to match a variable's distribution
Getting ready How to do it... How it works... There's more... See also
Searching for similar records using a Neural Network for inexact matching
Getting ready How to do it... How it works... There's more... See also
Using neuro-fuzzy searching to find similar names
Getting ready How to do it... How it works... There's more... See also
Producing longer Soundex codes
Getting ready How to do it... How it works... There's more... See also
4. Data Preparation – Construct
Introduction Building transformations with multiple Derive nodes
Getting ready How to do it... How it works... There's more...
Calculating and comparing conversion rates
Getting ready How to do it... How it works... There's more... See also
Grouping categorical values
Getting ready How to do it... How it works... There's more...
Transforming high skew and kurtosis variables with a multiple Derive node
Getting ready How to do it... How it works... There's more...
Creating flag variables for aggregation
Getting ready How to do it... How it works... There's more...
Using Association Rules for interaction detection/feature creation
Getting ready How to do it... How it works... There's more...
Creating time-aligned cohorts
Getting ready How to do it... How it works... There's more...
5. Data Preparation – Integrate and Format
Introduction Speeding up merge with caching and optimization settings
Getting ready How to do it... How it works... See also
Merging a lookup table
Getting ready How to do it... How it works... See also
Shuffle-down (nonstandard aggregation)
Getting ready How to do it... How it works... There's more... See also
Cartesian product merge using key-less merge by key
Getting ready How to do it... How it works... There's more... See also
Multiplying out using Cartesian product merge, user source, and derive dummy
Getting ready How to do it... How it works... There's more... See also
Changing large numbers of variable names without scripting
Getting ready How to do it... How it works... There's more... See also
Parsing nonstandard dates
Getting ready How to do it... How it works... There's more...
Nesting functions into one Derive node Performing clean downstream of a calculation using a Filter node Using parameters instead of constants in calculations
See also
Parsing and performing a conversion on a complex stream
Getting ready How to do it... How it works... See also
Sequence processing
Getting ready How to do it... How it works... There's more... See also
6. Selecting and Building a Model
Introduction Evaluating balancing with Auto Classifier
Getting ready How to do it... How it works... See also
Building models with and without outliers
Getting ready How to do it... How it works... See also
Using Neural Network for Feature Selection
Getting ready How to do it... How it works... There's more... See also
Creating a bootstrap sample
Getting ready How to do it... How it works... There's more... See also
Creating bagged logistic regression models
Getting ready How to do it... How it works... There's more... See also
Using KNN to match similar cases
Getting ready How to do it... How it works... See also
Using Auto Classifier to tune models
Getting ready How to do it... How it works... See also
Next-Best-Offer for large datasets
Getting ready How to do it... How it works... There's more... See also
7. Modeling – Assessment, Evaluation, Deployment, and Monitoring
Introduction How (and why) to validate as well as test
Getting ready How to do it... How it works... See also
Using classification trees to explore the predictions of a Neural Network
Getting ready How to do it... How it works... See also
Correcting a confusion matrix for an imbalanced target variable by incorporating priors
Getting ready How to do it... How it works... There's more... See also
Using aggregate to write cluster centers to Excel for conditional formatting
Getting ready How to do it... How it works... See also
Creating a classification tree financial summary using aggregate and an Excel Export node
Getting ready How to do it... How it works... See also
Reformatting data for reporting with a Transpose node
Getting ready How to do it... How it works... There's more... See also
Changing formatting of fields in a Table node
Getting ready How to do it... How it works... There's more... See also
Combining generated filters
Getting ready How to do it... How it works... There's more... See also
8. CLEM Scripting
Introduction
CLEM scripting best practices CLEM scripting shortcomings
Building iterative Neural Network forecasts
Getting ready How to do it... How it works...
Script section 1
There's more...
Quantifying variable importance with Monte Carlo simulation
Getting ready How to do it... How it works...
Script section 1 Script section 2
There's more...
Implementing champion/challenger model management
Getting ready How to do it... How it works...
Script section 1 Script section 2
There's more...
Detecting outliers with the jackknife method
Getting ready How to do it... How it works...
Script section 1 Script section 2 Script section 3
There's more...
Optimizing K-means cluster solutions
Getting ready How to do it... How it works...
Script section 1 Script section 2 Script section 3 Script section 4
There's more...
Automating time series forecasts
Getting ready How to do it... How it works...
Script section 1 Script section 2
There's more...
Automating HTML reports and graphs
Getting ready How to do it... How it works...
Script section 1 Script section 2 Script section 3
There's more...
Rolling your own modeling algorithm – Weibull analysis
Getting ready How to do it... How it works...
Script section 1
There's more...
A. Business Understanding
Introduction
What decisions are you trying to make using data?
Define business objectives by Tom Khabaza
The importance of business objectives in data mining Defining the business objectives of a data mining project
Understanding the goals of the business Understanding the objectives of your client Connecting specific objectives to analytical results Specifying your data mining goals
Assessing the situation by Meta Brown
Taking inventory of resources Reviewing requirements, assumptions, and constraints Identifying risks and defining contingencies Defining terminology Evaluating costs and benefits
Translating your business objective into a data mining objective by Dean Abbott
The key to the translation – specifying target variables Data mining success criteria – measuring how good the models actually are
Success criteria for classification Success criteria for estimation Other customized success criteria
Produce a project plan – ensuring a realistic timeline by Keith McCormick
Business understanding Data understanding Data preparation Modeling Evaluation Deployment
Index
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