7.00.02 - Random Forest Functions - Aster Analytics

Teradata Aster® 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
dita:mapPath
uce1497542673292.ditamap
dita:ditavalPath
AA-notempfilter_pdf_output.ditaval
dita:id
zuk1466006200888

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.