Publication date: 07/24/2024
Partition Models
Use Decision Trees to Explore and Model Your Data
The Partition platform recursively partitions data according to a relationship between the predictors and response values, creating a decision tree. The partition algorithm searches all possible splits of predictors to best predict the response. These splits (or partitions) of the data are done recursively to form a tree of decision rules. The splits continue until the desired fit is reached. The partition algorithm chooses optimum splits from a large number of possible splits, making it a powerful modeling, and data discovery tool.
Figure 4.1 Example of a Decision Tree
Contents
Overview of the Partition Platform
Example of the Partition Platform
Launch the Partition Platform
Informative Missing
The Partition Report
Partition Plot
Partition Buttons
Summary of Fit Statistics
Node Reports
Partition Platform Options
Show Fit Details
Specify Profit Matrix
ROC Curve
Precision-Recall Curve
Lift Curve
Validation in Partition
Additional Examples of the Partition Platform
Example of a Continuous Response
Example of Informative Missing
Example of Profit Matrix and Decision Matrix Report
Statistical Details for the Partition Platform
Statistical Details for Responses and Factors
Statistical Details for the Splitting Criterion
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