03-22-2012 11:07 AM
I am modeling the Target “1” which just happens 5% of the time (70 observations for Target "1"). Hence I did the oversampling and adjusting the priorities as follows.
-I add a sample node to the DataSource (the originalpopulation N=1374) in the new diagram (without partition node)
-I add a SCORE Node to the model selected by the bestmodel node
-I add a DECISION node following the modeling node(select model)
At the decision node I set the prior probabilities as:
a) Level “1”, Count (70), Prior (0.5), Adjusted Prior(0.05)
b) Level “1”, Count (70), Prior (0.5), Adjusted Prior(0.95)
c) I applied the decisions by setting “yes” and I runthis node
Then, I run again the score node at the diagram, as the results are below.
The event classification table at the Decision node shows the following results:
FN (70), TN (70), FP (0) TP (0)
How I can imporve my predictive model when i am prediting a RARE EVENT and my sample size is not large?
03-23-2012 10:45 AM
This thread may have an answer for you.
Otherwise, repost to the Data Mining Forum, so the people who use EM (I don't currenty) will see the question.