JMP 14.0 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Predictive and Specialized Modeling
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
JMP Interactive HTML
Capabilities Index
JMP 13 Online Documentation
JMP 12 Online Documentation
Predictive and Specialized Modeling
• Bootstrap Forest
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Bootstrap Forest
Fit a Model By Averaging Many Trees
The Bootstrap Forest platform is available only in JMP Pro.
The Bootstrap Forest platform fits an ensemble model by averaging many decision trees each of which is fit to a bootstrap sample of the training data. Each split in each tree considers a random subset of the predictors. In this way, many weak models are combined to produce a more powerful model. The final prediction for an observation is the average of the predicted values for that observation over all the decision trees.
Figure 5.1
Example of a Cumulative Validation Report
Contents
Overview of the Bootstrap Forest Platform
Example of Bootstrap Forest with a Categorical Response
Bootstrap Forest Model
Missing Values
Example of Bootstrap Forest with a Continuous Response
Launch the Bootstrap Forest Platform
Launch Window
Specification Window
The Bootstrap Forest Report
Model Validation-Set Summaries
Specifications
Overall Statistics
Cumulative Validation
Per-Tree Summaries
Bootstrap Forest Platform Options
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Help created on 7/12/2018