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Using JMP® Pro to Build Models Using Generalized Regression Variable Selection Techniques

 

See how to:

• Build a response surface model for as many terms as needed
• Examine Parameter Estimates VIF (Variable Inflation Factor) to identify highly correlated factors
• Examine Prediction Profiler to identify collinearity
• Publish prediction formulas to Formula Depot for subsequent model comparison
• Use validation to avoid model overfitting
• Determine when and how to use advanced control options, including enforcing effect heredity, forcing terms, and early stopping
• Use Variable Importance setting to organize Prediction Profiler by the most important factors
• Build and compare models using Forward Selection (Stepwise), Ridge, Lasso, Double  Lasso, Elastic Net and the adaptive Elastic Net regularization techniques to help identify key explanatory factors

Resources

• Building Better Models with JMP Pro by Grayson, Gardner and Stephens

• Files, including Production data not available in JMP Pro from JMP>Help>Sample Data, used in live Mastering JMP webcast, Using JMP Pro to Build Models Using Generalized Regression Variable Selection Techniques. 

   

Predictive Modeling and Machine Learning