Watch now 19:49
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
Part 1: Building Classification Trees using JMP Partition
Get a definition of tree-based models and some of the reasons to use them. See how to use JMP Partition to build models for categorical responses.
Part 2: Building Bootstrap Forests and Specifying Profit Matrices
See how to use JMP Pro Bootstrap Forests (random-forest techniques) for predictor screening and modeling. A short case study shows how you might use the JMP Pro Profit Matrix to make tradeoffs between avoiding undesirable outcomes and obtaining desirable outcomes.
Part 3: Building Boosted Trees
See how to use JMP Pro Boosted Trees for predictor screening and modeling.