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

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

3. 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

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

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

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

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

8. 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

9. 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

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

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

12. 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

13. Model Complexity 13...

14. Model Complexity 14 Not complex enough...

15. Model Complexity 15 Too complex Not complex enough

16. Honest Assessment: Data Splitting 16

17. Data Partitioning 17 Training Data inputstarget...

18. Data Partitioning 18 Training DataValidation Data inputstargetinputstarget...

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

20. 543 2 1 Predictive Model Sequence 20 Create a sequence of models with increasing complexity. Model Complexity Training DataValidation Data inputstargetinputstarget

21. Model Performance Assessment 21 Validation Assessment Rate model performance using validation data. Training DataValidation Data inputstargetinputstarget 543 2 1 Model Complexity

22. 54 2 13 Model Selection 22 Model Complexity Validation Assessment Select the simplest model with the highest validation assessment. Training DataValidation Data inputstargetinputstarget

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

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

25. Accessing the Neural or Partition Platforms 25

26. Partition Platform Dialog 26

27. Neural Platform Dialog 27