Two ways to score validation data in proc logistic

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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4 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

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