Hi, I am Angela. I have been working on developing a logistic regression model and using the model comparison tool to compare models. I have been troubled by errors in the model comparison tool. I have tried to change the variables and punctuations but it doesn't help to solve the errors.
The error: Model Comparison: Tool #3: Error in names(scores) <- paste(score.field, "_", y.levels, sep = "") :
Any help would be appreciated!
Please find attached the workflow and input file. Thanks.
Solved! Go to Solution.
Hi Please. Thanks.
Perhaps can you send me the file again and see if I can run it or not? Millions of thanks.
It does not work 😞
Would you mind capturing me the report for logistic regression, stepwise and model comparison? As well as making a decision tree and model comparison.
Sorry I know I am asking a bit more but this workflow is really important to me as it is an urgent work that I have to analyse within hours. Millions of thanks,
Great work, @apathetichell! ConnectNamedPipe errors are quite often environmental, so I recommend reaching out to our Support team. They'll know the right questions to ask in terms of whitelisting, anti virus software (McAfee can cause problems), etc. Cheers!
It will be much appreciated if you can also run the score for the models.
Thank you!
Do you prefer the Tree model? I think the stepwise is currently hooked up. also - I could run both if you want- and post the output as .xlsx. Also you wanted the model run with all the data- no? I'm no expert in these things but with the thresholds for your Tree set so low (to include the West Region values), I'd want to scrutinize the model for overfitting.
For future R workflows, it might be worth reinstalling R and even turning off anti-virus when running some of the workflows. As @CristonS mentioned - R is very machine/environment specific. You can also do test runs of your data in Rstudio to see if its flagging the same errors.
I prefer running both Decision Tree and Stepwise models. I don’t think all the data should be run as I saw that the errors are high. And as I remembered, the root node errors are high, not sure how can I reduce the errors.
Yes you could scrutinize the model for overfitting.
Thanks.
the .xlsx has both with the stepwise and the decision tree run on all the data and labeled as such. If you want it just run on the eval/test data let me know and I can set that up too. Also I could render if if you want more readable excel data.