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 aggregateand 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