Chapter 1: Introduction to Predictive Modeling 1.1 Applications 1.2 Generalization 1.3 JMP Predictive Modeling Platforms

Chapter 1: Introduction to Predictive Modeling 1.1 Applications 1.2 Generalization 1.3 JMP Predictive Modeling Platforms

Objectives Describe common applications of predictive modeling in business, science, and engineering. Describe typical data that is available for predictive modeling. Define commonly used terms used in predictive modeling. 3

Predictive Modeling Applications 4 Database marketing Financial risk management Fraud detection Process monitoring Pattern detection Healthcare Informatics

The Data 5 ExperimentalOpportunistic Purpose Research Operational Value Scientific Commercial Generation Actively controlled Passively observed Size Small Massive Hygiene Clean Dirty State Static Dynamic

inputstarget Predictive Modeling Data 6 Training Data Training data case: categorical or numeric input and target measurements

Types of Targets Supervised Classification –Event/no event (binary target) –Class label (multiclass problem) Regression –Continuous outcome 7

Continuous Targets Healthcare Outcomes –Target = hospital length of stay, hospital cost Liquidity Management –Target = amount of money at an ATM machine or in a branch vault Process Volatility –Target = moving range of yields Sales –Target = dollar value of sales 8

Measurement Levels Three types in JMP Continuous Ordinal Nominal JMP automatically performs specific types of analyses based on the measurement level of the target. For example, linear regression versus logistic regression. In some platforms, ordinal and nominal variables inputs are handled differently. 9

Chapter 1: Introduction to Predictive Modeling 1.1 Applications 1.2 Generalization 1.3 JMP Predictive Modeling Platforms

Objectives Define generalization. Define honest assessment. Describe how honest assessment can be done in JMP. 11

The Scope of Generalization Model Selection and Comparison –Which model gives the best prediction? Decision/Allocation Rule –What actions should be taken on new cases? Deployment –How can the predictions be applied to new cases? 12

Model Complexity 13...

Model Complexity 14 Not complex enough...

Model Complexity 15 Too complex Not complex enough

Honest Assessment: Data Splitting 16

Data Partitioning 17 Training Data inputstarget...

Data Partitioning 18 Training DataValidation Data inputstargetinputstarget...

Data Partitioning 19 Training DataValidation Data Partition available data into training and validation sets. inputstargetinputstarget

Predictive Model Sequence 20 Create a sequence of models with increasing complexity. Model Complexity Training DataValidation Data inputstargetinputstarget

Model Performance Assessment 21 Validation Assessment Rate model performance using validation data. Training DataValidation Data inputstargetinputstarget Model Complexity

Model Selection 22 Model Complexity Validation Assessment Select the simplest model with the highest validation assessment. Training DataValidation Data inputstargetinputstarget

Chapter 1: Introduction to Predictive Modeling 1.1 Applications 1.2 Generalization 1.3 JMP Predictive Modeling Platforms

Objectives Show the platforms that will used in the class. 24

Accessing the Neural or Partition Platforms 25

Partition Platform Dialog 26

Neural Platform Dialog 27