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.
relative_error = (abs(mle_prediction - td_prediction)/mle_prediction)*100where mle_prediction is ML Engine prediction value and td_prediction is Advanced SQL Engine prediction value. Errors (e) follow Gaussian law; 0 < e < 3% is a negligible difference, with high confidence.