Two ways to score validation data in proc logistic

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This article explains two ways to score a validation dataset in PROC LOGISTIC.

1. SCORE Option in PROC LOGISTIC

Proc Logistic Data = training;
Model Sbp_flag = age_flag bmi_flag/ lackfit ctable pprob =0.5;
Output out= test p=ppred;
Score data=validation out = Logit_File;
Run;

2. OUTMODEL / INMODEL Option in PROC LOGISTIC

Proc Logistic Data = training outmodel= model;
Model Sbp_flag = age_flag bmi_flag/ lackfit ctable pprob =0.5;
Output out= test p=ppred;
Run;

proc logistic inmodel=model;
score data=validation out=valid;
run;

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Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has close to 7 years of experience in data science and predictive modeling. During his tenure, he has worked with global clients in various domains like retail and commercial banking, Telecom, HR and Automotive.


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5 Responses to "Two ways to score validation data in proc logistic"

  1. Pls when is the best time to split a data set into training and validation - at the begining after forming the modeling data set or after cleaning the data (missing value imputation and outlier treatment)?

    ReplyDelete
  2. Pls when is the best time to split a data set into training and validation - at the begining after forming the modeling data set or after cleaning the data (missing value imputation and outlier treatment)?

    ReplyDelete
  3. i split the data after cleaning the data , after missing value imputation but before outlier treatment. I do outlier treatment , during variable transformation, after initial run of proc logistic.

    ReplyDelete
  4. split the data into training & modeling after cleaning,removing missing values and outlier, transformation. After that we run the proc logistic model.

    ReplyDelete
  5. the predicted value we get from that is that the odds ratio?

    ReplyDelete

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