Publication date: 08/13/2020

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 

Contents

Overview of the Partition Platform

Example of the Partition Platform

Launch the Partition Platform

The Partition Report

Control Buttons
Report for Categorical Responses
Report for Continuous Responses

Partition Platform Options

Show Fit Details
Specify Profit Matrix
Decision Matrix Report
Informative Missing
Actual by Predicted Plot
ROC Curve
Lift Curve
Node Options

Validation

K-Fold Crossvalidation

Additional Examples of Partitioning

Example of a Continuous Response
Example of Informative Missing
Example of Profit Matrix and Decision Matrix Report

Statistical Details for the Partition Platform

Responses and Factors
Splitting Criterion
Predicted Probabilities in Decision Tree and Bootstrap Forest
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