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[图书]SPSS Modeler Cookbook

[图书]SPSS Modeler Cookbook

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http://dgdsbygo8mp3h.cloudfront.net/sites/default/files/imagecache/productview_larger/5467EN.jpgTableofContentsPrefaceChapter1:DataUnderstandingChapter2:DataPreparation–SelectChapter3:DataPreparation ...
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Table of Contents
Preface
Chapter 1: Data Understanding
Chapter 2: Data Preparation – Select
Chapter 3: Data Preparation – Clean
Chapter 4: Data Preparation – Construct
Chapter 5: Data Preparation – Integrate and Format
Chapter 6: Selecting and Building a Model
Chapter 7: Modeling – Assessment, Evaluation, Deployment, and Monitoring
Chapter 8: CLEM Scripting
Appendix: Business Understanding
Index
  • Preface
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Appendix: 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

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