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Altair Community Member
OPOkay, Sara I took your suggestion and went to
https://docs.rapidminer.com/9.5/studio/guided/auto-model/
where half way down the documentation page is this:
Auto Model provides you with a selection of models that are relevant for your problem. If there is no time constraint, the best option is probably to build all of them, and compare their performance once they are finished.
That sounds like the solution for my data prep question.
I'm a newbie and dragging my way through the Studio tutorials. Why should I continue to learn the basics when I have Auto Model? What am I missing here?
Thank you for your time.
Tony
https://docs.rapidminer.com/9.5/studio/guided/auto-model/
where half way down the documentation page is this:
Auto Model provides you with a selection of models that are relevant for your problem. If there is no time constraint, the best option is probably to build all of them, and compare their performance once they are finished.
That sounds like the solution for my data prep question.
I'm a newbie and dragging my way through the Studio tutorials. Why should I continue to learn the basics when I have Auto Model? What am I missing here?
Thank you for your time.
Tony
New Altair Community Member
Accepted Answer
Hi Tony,
That's a very interesting question. I believe that in the future, Auto Model or similar technologies will automate most of the modelling part of a data science pipeline. But we are not there yet.
Auto Model tries multiple models with recommended parameters using a heuristic, and it only uses a portion of the data in order to train all those models in a reasonable time. Once you find a promising model with Auto Model, you should still optimize the process using all the data. There are several aspect that can still be optimized:
* Model selection
* Variable selection
* Hyper-parameter optimization
As such I think of Auto Model as a prototyping tool, still far away from a production model.
Best,
Sebastian
That's a very interesting question. I believe that in the future, Auto Model or similar technologies will automate most of the modelling part of a data science pipeline. But we are not there yet.
Auto Model tries multiple models with recommended parameters using a heuristic, and it only uses a portion of the data in order to train all those models in a reasonable time. Once you find a promising model with Auto Model, you should still optimize the process using all the data. There are several aspect that can still be optimized:
* Model selection
* Variable selection
* Hyper-parameter optimization
As such I think of Auto Model as a prototyping tool, still far away from a production model.
Best,
Sebastian
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Accepted Answer
@tonyboy9
Hello
From my understanding if you use Auto Model, It will choose best algorithms for your data and make the process.
I hope this helps
Sara
Hello
From my understanding if you use Auto Model, It will choose best algorithms for your data and make the process.
I hope this helps
Sara
New Altair Community Member
Accepted Answer
Hi Tony,
That's a very interesting question. I believe that in the future, Auto Model or similar technologies will automate most of the modelling part of a data science pipeline. But we are not there yet.
Auto Model tries multiple models with recommended parameters using a heuristic, and it only uses a portion of the data in order to train all those models in a reasonable time. Once you find a promising model with Auto Model, you should still optimize the process using all the data. There are several aspect that can still be optimized:
* Model selection
* Variable selection
* Hyper-parameter optimization
As such I think of Auto Model as a prototyping tool, still far away from a production model.
Best,
Sebastian
That's a very interesting question. I believe that in the future, Auto Model or similar technologies will automate most of the modelling part of a data science pipeline. But we are not there yet.
Auto Model tries multiple models with recommended parameters using a heuristic, and it only uses a portion of the data in order to train all those models in a reasonable time. Once you find a promising model with Auto Model, you should still optimize the process using all the data. There are several aspect that can still be optimized:
* Model selection
* Variable selection
* Hyper-parameter optimization
As such I think of Auto Model as a prototyping tool, still far away from a production model.
Best,
Sebastian
Hello
From my understanding if you use Auto Model, It will choose best algorithms for your data and make the process.
I hope this helps
Sara