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Publication date: 06/21/2023

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 TreeĀ 

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
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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