The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
H2O error logistic regression
Hello,
I am a student using RM for my education.
I have a dataset from internet and want to use logistic regression on it but when running the model I get this message: Model training error (H2O) illegal arguments for GLM mode: ERRR of field etc etc.
Does somebody know what i did wrong?
Best regards,
Robert
I am a student using RM for my education.
I have a dataset from internet and want to use logistic regression on it but when running the model I get this message: Model training error (H2O) illegal arguments for GLM mode: ERRR of field etc etc.
Does somebody know what i did wrong?
Best regards,
Robert
Tagged:
0
Best Answer
-
varunm1 Member Posts: 1,207 UnicornHello @Roeieber
The error implies that you are trying to train that model on a dataset that has a single label. I know you are not really trying to do this, but the data entering into the logistic regression is like that.
Reasons:
1. You did a split validation on data that is highly imbalanced. I recommend you select stratified sampling for this after setting a label role for a column in our data.
2. Also, you made the process a bit complex by adding a set role and numerical to binomial operators after the splitting of data. This is not necessary and also has some issues while encoding numerical to binomial.
I changed the process and attached it. Import it, test it and inform if you need more information.
Hope this helps.Regards,
Varun
https://www.varunmandalapu.com/Be Safe. Follow precautions and Maintain Social Distancing
7
Answers
Thanks for your help.
I had to take out the numerical to binomial operator, but then it worked.
I also read about the stratified split and understand why this could be better.Â
Thnk you again.
Best regards,
Robert