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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 Applications1.1 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.
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Predictive Modeling Applications
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Database marketing
Financial risk management
Fraud detection
Process monitoring
Pattern detection
Healthcare Informatics
The Data
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Experimental Opportunistic
Purpose Research Operational
Value Scientific Commercial
Generation Actively controlled Passively observed
Size Small Massive
Hygiene Clean Dirty
State Static Dynamic
inputs target
Predictive Modeling Data
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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
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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
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Measurement LevelsThree 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.
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Chapter 1: Introduction to Predictive Modeling
1.1 Applications
1.2 Generalization1.2 Generalization
1.3 JMP Predictive Modeling Platforms
Objectives Define generalization. Define honest assessment. Describe how honest assessment can be done in JMP.
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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?
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Model Complexity
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Model Complexity
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Not complex enough
...
Model Complexity
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Too complex
Not complex enough
Honest Assessment: Data Splitting
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Data Partitioning
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Training Data
inputs target
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Data Partitioning
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Training Data Validation Data
inputs target inputs target
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Data Partitioning
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Training Data Validation Data
Partition available data intotraining and validation sets.
inputs target inputs target
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Predictive Model Sequence
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Create a sequence of models with increasing complexity.
ModelComplexity
Training Data Validation Data
inputs target inputs target
Model Performance Assessment
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ValidationAssessment
Rate model performance using validation data.
Training Data Validation Data
inputs target inputs target
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ModelComplexity
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Model Selection
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ModelComplexity
ValidationAssessment
Select the simplest model with the highest validation assessment.
Training Data Validation Data
inputs target inputs target
Chapter 1: Introduction to Predictive Modeling
1.1 Applications
1.2 Generalization
1.3 JMP Predictive Modeling Platforms1.3 JMP Predictive Modeling Platforms
Objectives Show the platforms that will used in the class.
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Accessing the Neural or Partition Platforms
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Partition Platform Dialog
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Neural Platform Dialog
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