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Logistic Regression - applying model equation to source data

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Asteroid

I'd like to use my logistic regression equation to show which items in my source data would have been predicted to be positive, and which would have been predicted to be negative.  My target variable is: Does the employee term within 90 days?  YES/NO.  Here's the regression model formula:

 

glm(formula = TERM_90DAYS ~ DOMICILE + VET + BUS_UNIT + AGE_AT_HIRE + MAR_STATUS + SCHOOL_LEVEL + HOURLY_RT, family = binomial("logit"), data = the.data)

 

Using this formula and the coefficients, I'd like to feed in the source data to the model so I will be able to see which teammates would have been correctly predicted to term within 90 days, which would have been correctly predicted to not term within 90 days, my Type I errors, and my Type II errors.

 

So, basically, my source data has 4,058 records, and I'd like to know which of those records fall within the four quadrants shown below (from the I output on my Logistic Regression tool).  I'd like the process to be as automated as possible.  Is there an easy way to do this?

 

Capture.PNG

Alteryx
Alteryx

Hi @oracleoftemple

 

The output you are looking for can be obtained by putting a Browse tool after the output anchor on the Logistic Regression tool. You can see a sample of this by opening the following sample workflow:

 

Help -> Sample Workflows -> Predictive Tool Samples -> Predictive Analytics -> Logistic Regression

 

Run this workflow and click the Browse tool after the I anchor.

 

image.png

 

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