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Compare ROCs - alternative for polynominal labels?
frank_claessen
Member Posts: 2 
Learner I
in Help
Hi all,
This is my first question here.
Mind you, it is not necessarily forcing a roc-chart on a polynominal label but just running multiple models on the same dataset and after that finding some way of comparing accuracy, recall etc. from the respective applied models.
And - the AI I just consulted says there is no operator to apply more than one model like the way ROCS does. You just have to work around it by:
<citing gemini now>
"
I hope to find people "smarter" than gemini
TIA - Frank
This is my first question here.
Mind you, it is not necessarily forcing a roc-chart on a polynominal label but just running multiple models on the same dataset and after that finding some way of comparing accuracy, recall etc. from the respective applied models.
And - the AI I just consulted says there is no operator to apply more than one model like the way ROCS does. You just have to work around it by:
<citing gemini now>
"
- Convert Labels:
- Use the "Nominal to Binominal" operator. This simplifies your problem to a binary classification by combining multiple label classes into two. However, this approach loses information about the original class distribution.
- One-vs-Rest Approach:
- Build separate models for each unique class in the polynomial label. Each model predicts that class as positive and all others as negative.
- Use "Compare ROCs" on these individual models. This provides a more comprehensive view of model performance for each class.
I hope to find people "smarter" than gemini
TIA - Frank
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