Sign up to take part
Registered users can ask their own questions, contribute to discussions, and be part of the Community!
Registered users can ask their own questions, contribute to discussions, and be part of the Community!
Hello,
Using prediction model in the lab, we noticed that each time our input dataset is regenerated/updated the current model settings are updated by Dataiku ?
For instance, some features are activated/included in the model whereas they were deactivated for the last train session. Is there an option/setting which can avoid this behavior ?
It would be very helpful as it's quite time consuming to review systematically all the features settings each time we update the input dataset.
Annie
Hi,
Could you please share a screenshot example of a ml model where the feature activation changed after you updated your underlying dataset? Did the meaning of that feature change? For example from text to bigint?
Hello,
I don't have an example at this time as we finalized the model.
In our case, the input dataset is generated with a Python recipe. And we noticed that any change on this input dataset may switch a feature from inactive (in the model) to active without changing the data type.
Moreover, most of the features have the buttons 'Accept' or 'Keep my settings' in the design/features handling screen. It can be very cumbersome to process feature by feature when you have a long list of features in the model.
Please, do you know in which context this automatic change happens ? is there a way to avoid it ?
Annie