Random Forest Functions - Aster Analytics

Teradata AsterĀ® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Language
English (United States)
Last Update
2018-04-17
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uce1497542673292.ditamap
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dita:id
B700-1022
lifecycle
previous
Product Category
Software

The random forest functions create a predictive model based on a combination of the Classification and Regression Trees (CART) algorithm for training decision trees and the ensemble learning method of bagging.



The random forest functions are:

  • Forest_Drive, which builds a predictive model based on training data.
  • Forest_Predict, which uses the model generated by the Forest_Drive function to analyze the input data and make predictions.
  • Forest_Analyze, which analyzes the model generated by the Forest_Drive function and gives weights to the variables used in the model. This helps you understand the basis by which the Forest_Predict function makes predictions.

You can use the Forest_Drive and Forest_Predict functions to generate predictions input for the Receiver Operating Characteristic (ROC) function.