16.20 - DecisionForestPredict - Teradata Database - Teradata Vantage NewSQL Engine

Teradata Vantage™ - NewSQL Engine Analytic Functions

Teradata Database
Teradata Vantage NewSQL Engine
Release Number
Release Date
July 2019
Content Type
Programming Reference
Publication ID
English (United States)

The DecisionForestPredict function uses the model output by ML Engine DecisionForest function to analyze the input data and make predictions.

If your model table was created using a supported version of Aster Analytics on Aster Database, see AA 7.00 Usage Notes.

DecisionForestPredict outputs the probability that each observation is in the predicted class. To use DecisionForestPredict output as input to ML Engine ROC function, you must first transform it to show the probability that each observation is in the positive class. One way to do this is to change the probability to (1- current probability) when the predicted class is negative.
The prediction algorithm compares floating-point numbers. Due to possible inherent data type differences between ML Engine and NewSQL Engine executions, predictions can differ. Before calling the function, compute the relative error, using this formula:
relative_error = (abs(mle_prediction - td_prediction)/mle_prediction)*100

where mle_prediction is ML Engine prediction value and td_prediction is NewSQL Engine prediction value. Errors (e) follow Gaussian law; 0 < e < 3% is a negligible difference, with high confidence.