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Building Better Predictive Models Using JMP Pro

 

See how to:

  • Understand JMP Pro predictive modeling capabilities and basic predictive model types
  • Understand suggested workflow and which model to use at each step
  • Learn about the Diabetes Disease Progression case study data
  • Follow a modeling workflow that describes suggested models to use at each step
  • Use cross-validation to prevent overfitting candidate models and to help compare multiple candidate models
  • Use variable selection to determine variables to include in prediction model
  • Build Generalized Regression model using Adaptive Lasso estimation method
  • Build multiple candidate prediction models (Bootstrap Forest and Boosted Tree) that include risk factors identified during variable selection
    • Create validation column
    • Assess each model by comparing training and validation set results
    • Compare multiple models, including test set results, using R-squared, RMSE, Actual-by-Predicted, and Model Averaging
  • Deploy chosen model
  • Save  chosen model score code to Formula Depot to optionally deploy outside JMP

Note: Q&A included at times 45:55, 46:51, 47:18, 47:56, 49:52, 50:25 and 50:49.

 

2 Coombs Predictive Models.jpg

 

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Use Videos and Resources to Practice JMP, JMP Pro, JMP Clinical and JMP Genomics.

1-hour live Mastering JMP webinars occur most Fridays from January through October. After each session, we hope you will use the video and resources shared by the presenting JMP Systems Engineers to practice what you saw.

Mastering JMP Videos are available here.