Decision Tree Structure | Vantage Analytics Library - Decision Tree Structure - Vantage Analytics Library

Vantage Analytics Library User Guide

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
Lake
VMware
Product
Vantage Analytics Library
Release Number
2.2.0
Published
March 2023
Language
English (United States)
Last Update
2024-01-02
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Product Category
Teradata Vantage

A decision tree model has a root node, which is associated with all the training data set used to build the tree. Each tree node is either a decision node or a leaf node.

A decision node represents a split in the data based on the value of a single input or predictor variable. A decision node has descendant nodes.

A leaf node represents a subset of the data that has a particular value of the predicted variable (for example, the resulting class of the predicted variable) and a measure of accuracy. A leaf node has no descendent nodes.

When building a decision tree, consider the following:
  • How to split data at each decision node
  • When to stop splitting each decision node and make it a leaf
  • Which class to assign to each leaf node
It is usually best to let a decision tree grow as big as necessary and then prune it to reduce its complexity and increase its interpretability.

Typically, you use a decision tree to score or classify new data, creating a new table containing key fields and the predicted value or class identifier. However, if the new data includes the values of the predicted variable, you can use those values to measure the effectiveness of the decision tree.