Understanding and Applying Tree-based Methods for Predictor Screening and Modeling

Presenter: Peter Hersh

Modeling using JMP Partition, Bootstrap Forests and Boosted Trees

See how to:

  • Understand pros and cons of decision trees.
    • Pros: Uncover non-linear relationships, get results that are easy to understand, screen large number of factors
    • Cons: Handle one response at a time, forms if-then statement not mathematical formula, high variability can lead to major differences in a model for similar data
  • Build a JMP Partition classification tree that uses splitting to define relationship between the predictors and categorical responses
  • Define the number of samples, size of the trees (models) and sample rate to build Bootstrap Forest models using a random-forest technique
  • Define the number of layers, splits per tree, learning rate and sampling rates to build Boosted Tree models that combine smaller models into a final model
  • Interpret results and tune models
  • Specify and run Profit Matrix reports to identify misclassifications for categorical responses, and interpret results using Confusion Matrix and Decision Matrix

Note: Q&A included at time 17:00 and time 38:00


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