BookmarkSubscribeRSS Feed
andreas_zaras
Pyrite | Level 9


Hello,

I have to predict a binary variable (1/0) using predicitve modelling. ANN, Logistc Regression and Decidion Trees are used. In the socring code there are two variables created that i don;t know how to interpret. These are I_TARGET_B and U_TARGET_B. WHat do these variables express? As i have seen these variables take the same values, the one as character and the other as numeric). The scoring code for these variables is as follows:

 

*** Writing the I_TARGET_B  AND U_TARGET_B ;

  *** *************************;

  _MAXP_ = P_TARGET_B1 ;

  I_TARGET_B = "1           " ;

  U_TARGET_B=                    1;

  IF( _MAXP_ LT P_TARGET_B0  ) THEN DO;
_MAXP_ = P_TARGET_B0 ;
I_TARGET_B  = "0           "

U_TARGET_B  =                    0;

  END;

  ********************************;

  *** End Scoring Code for Neural;

  ********************************;

What i understand is that _MAXP_ is eqaukt ot the probability of the primary event. Then the values of I_TARGET_B is set to 1 (character) and the values of U_TARGET_B is set to 1 (numeric). SOthese variables have the same values, the one is character, the other numeric. Then if the probability of promary event is lower than the probability of secondary event I_TARGET_B and U_TARGET_B take the  value of 0 (the one character, the other numeric).

What is the meaning of the variables (that practically are the same)?

Thnaksin advance,

ANdreas

1 REPLY 1
adjgiulio
Obsidian | Level 7

The meaning of the two variables is:

I_ -- normalized category that the case is classified into

U_ -- unnormalized category that the case is classified into

From a practical perspective I haven't come across cases where they differ. In your case the interval vs nominal format reflects the fact that your target is numeric (even though you have probably defined it as binary in metadata). If you were to use a GOOD/BAD binary target, I_ would also be nominal. U_ is always nominal.

G

Ready to join fellow brilliant minds for the SAS Hackathon?

Build your skills. Make connections. Enjoy creative freedom. Maybe change the world. Registration is now open through August 30th. Visit the SAS Hackathon homepage.

Register today!
How to choose a machine learning algorithm

Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.

Find more tutorials on the SAS Users YouTube channel.

Discussion stats
  • 1 reply
  • 891 views
  • 4 likes
  • 2 in conversation