score new data with sas enterprise miner

dee2017 — Wed, 13 Sep 2017 20:04:10 GMT

Hello,

I have some output from the score node. However, i dont know how to interpret it. Please can someone help with an explanation?

Please see code below.

Thanks

Dee

*------------------------------------------------------------*
User: user
Date: 13 September 2017
Time: 20:31:56 o'clock
*------------------------------------------------------------*
* Training Output
*------------------------------------------------------------*

Variable Summary

Measurement Frequency
Role Level Count

SEGMENT INTERVAL 1
SEGMENT NOMINAL 1
TARGET BINARY 1

*------------------------------------------------------------*
* Score Output
*------------------------------------------------------------*

Score Input Variables

Used in
Variable Score
Variable Name Role Creator Comment Label Hidden Code

WHERE_VERIFIED INPUT Y Y

Score Output Variables

Variable Name Function Creator Label

EM_CLASSIFICATION CLASSIFICATION Score Prediction for PATIENT_ALIVE_FLAG
EM_EVENTPROBABILITY PREDICT Score Probability for level 1 of PATIENT_ALIVE_FLAG
EM_PROBABILITY PREDICT Score Probability of Classification
EM_SEGMENT TRANSFORM Score Segment
IMP_WHERE_VERIFIED TRANSFORM Impt Imputed WHERE_VERIFIED
I_PATIENT_ALIVE_FLAG CLASSIFICATION EndGrp2 Into: PATIENT_ALIVE_FLAG
P_PATIENT_ALIVE_FLAG0 PREDICT EndGrp2 Predicted: PATIENT_ALIVE_FLAG=0
P_PATIENT_ALIVE_FLAG1 PREDICT EndGrp2 Predicted: PATIENT_ALIVE_FLAG=1
U_PATIENT_ALIVE_FLAG CLASSIFICATION EndGrp2 Unnormalized Into: PATIENT_ALIVE_FLAG
_WARN_ ASSESS EndGrp2 Warnings
_XVAL_ TRANSFORM Impt
b_PATIENT_ALIVE_FLAG TRANSFORM MdlComp
fold TRANSFORM Trans2

*------------------------------------------------------------*
* Report Output
*------------------------------------------------------------*

Class Variable Summary Statistics

Data Role=TRAIN Output Type=CLASSIFICATION

Numeric Formatted Frequency
Variable Value Value Count Percent

I_PATIENT_ALIVE_FLAG . 1 876 100

Data Role=TRAIN Output Type=SEGMENT

Numeric Formatted Frequency
Variable Value Value Count Percent

b_PATIENT_ALIVE_FLAG 1 1 51 5.82192
b_PATIENT_ALIVE_FLAG 2 2 56 6.39269
b_PATIENT_ALIVE_FLAG 3 3 54 6.16438
b_PATIENT_ALIVE_FLAG 4 4 43 4.90868
b_PATIENT_ALIVE_FLAG 5 5 41 4.68037
b_PATIENT_ALIVE_FLAG 6 6 38 4.33790
b_PATIENT_ALIVE_FLAG 7 7 30 3.42466
b_PATIENT_ALIVE_FLAG 8 8 43 4.90868
b_PATIENT_ALIVE_FLAG 9 9 41 4.68037
b_PATIENT_ALIVE_FLAG 10 10 40 4.56621
b_PATIENT_ALIVE_FLAG 11 11 39 4.45205
b_PATIENT_ALIVE_FLAG 12 12 55 6.27854
b_PATIENT_ALIVE_FLAG 13 13 41 4.68037
b_PATIENT_ALIVE_FLAG 14 14 43 4.90868
b_PATIENT_ALIVE_FLAG 15 15 54 6.16438
b_PATIENT_ALIVE_FLAG 16 16 31 3.53881
b_PATIENT_ALIVE_FLAG 17 17 49 5.59361
b_PATIENT_ALIVE_FLAG 18 18 40 4.56621
b_PATIENT_ALIVE_FLAG 19 19 46 5.25114
b_PATIENT_ALIVE_FLAG 20 20 41 4.68037

Data Role=VALIDATE Output Type=CLASSIFICATION

Numeric Formatted Frequency
Variable Value Value Count Percent

I_PATIENT_ALIVE_FLAG . 1 586 100

Data Role=VALIDATE Output Type=SEGMENT

Numeric Formatted Frequency
Variable Value Value Count Percent

b_PATIENT_ALIVE_FLAG 1 1 35 5.97270
b_PATIENT_ALIVE_FLAG 2 2 40 6.82594
b_PATIENT_ALIVE_FLAG 3 3 34 5.80205
b_PATIENT_ALIVE_FLAG 4 4 25 4.26621
b_PATIENT_ALIVE_FLAG 5 5 25 4.26621
b_PATIENT_ALIVE_FLAG 6 6 31 5.29010
b_PATIENT_ALIVE_FLAG 7 7 18 3.07167
b_PATIENT_ALIVE_FLAG 8 8 23 3.92491
b_PATIENT_ALIVE_FLAG 9 9 25 4.26621
b_PATIENT_ALIVE_FLAG 10 10 26 4.43686
b_PATIENT_ALIVE_FLAG 11 11 29 4.94881
b_PATIENT_ALIVE_FLAG 12 12 26 4.43686
b_PATIENT_ALIVE_FLAG 13 13 25 4.26621
b_PATIENT_ALIVE_FLAG 14 14 24 4.09556
b_PATIENT_ALIVE_FLAG 15 15 34 5.80205
b_PATIENT_ALIVE_FLAG 16 16 28 4.77816
b_PATIENT_ALIVE_FLAG 17 17 30 5.11945
b_PATIENT_ALIVE_FLAG 18 18 24 4.09556
b_PATIENT_ALIVE_FLAG 19 19 43 7.33788
b_PATIENT_ALIVE_FLAG 20 20 41 6.99659

Interval Variable Summary Statistics

Variable Name=P_PATIENT_ALIVE_FLAG0

Statistics Label TRAIN VALIDATE

MEAN Mean 0.034 0.038
STD Standard Deviation 0.046 0.049
N Non Missing 876.000 586.000
MIN Minimum 0.000 0.000
P25 25th Percentile 0.012 0.013
MEDIAN Median 0.021 0.024
P75 75th Percentile 0.038 0.042
MAX Maximum 0.751 0.458

Variable Name=P_PATIENT_ALIVE_FLAG1

Statistics Label TRAIN VALIDATE

MEAN Mean 0.966 0.962
STD Standard Deviation 0.046 0.049
N Non Missing 876.000 586.000
MIN Minimum 0.249 0.542
P25 25th Percentile 0.962 0.958
MEDIAN Median 0.979 0.976
P75 75th Percentile 0.988 0.987
MAX Maximum 1.000 1.000

Re: score new data with sas enterprise miner

DougWielenga — Fri, 15 Sep 2017 17:11:25 GMT

The output from the Score node will contain the following output variables:

    Fixed Output Name Definition
    EM_PREDICTION                         The prediction variable for an interval target.
    EM_PROBABILITY                        Posterior probability associated with the predicted classification. That is, it corresponds the maximum of the posterior probabilities, max(P1, P2, ..., Pk).
    EM_EVENTPROBABILITY            Posterior probability associated with target event.
EM_CLASSIFICATION                  I_variable, the prediction variable for a class target, the most likely outcome
    EM_SEGMENT                             name of the segment variable.

For your analysis, SAS Enterprise Miner generated

P_PATIENT_ALIVE_FLAG1: the probability that PATIENT_ALIVE_FLAG = 1

P_PATIENT_ALIVE_FLAG0: the probability that PATIENT_ALIVE_FLAG = 0

The assignment to an outcome is ultimately a modeling decision. If you just want to assign each observation to the most likely outcome, this is the value SAS Enterprise Miner has stored in I_PATIENT_ALIVE_FLAG. It is possible, however, that you might not want to predict PATIENT_ALIVE_FLAG = 0 unless there is a very high probability that this was the case (e.g. perhaps only when P_PATIENT_ALIVE_FLAG0 is greater than 0.9). In any case, the software can compute the probabilities but you need to decide what the threshold should be to determine if you predict PATIENT_ALIVE_FLAG=1 or PATIENT_ALIVE_FLAG=0. It is then easy to assign the observations to a category based on the cutoff you choose.

Hope this helps!

Doug

Re: score new data with sas enterprise miner

dee2017 — Fri, 15 Sep 2017 16:50:08 GMT

Okay. brilliant thank you very much.

Re: score new data with sas enterprise miner

AnnaBrown — Fri, 15 Sep 2017 17:15:00 GMT

Hi dee2017,

I'm glad you found some useful info! If Doug's reply was the exact solution to your problem, can you "Accept it as a solution"? Or if it was particularly helpful, feel free to "Like" it. This will help other community members who may run into the same issue know what worked.

Thanks!
Anna

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score new data with sas enterprise miner

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