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Detail about Machine Learning model in dataflow
May i know the detail regarding the Train numeric prediction in data flow. like :
- What is minimum node size means?
- What is maximum Depth means?
- What is Maximum confidence means?
- What is Train Partition percent change going to impact to?
- What is Balancing method under sample, over sample, none explanation?
Best Answers
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Please refer to the following blogs that describe many of these hyper parameters:
https://oracledataviz.blogspot.com/search?q=random+forest
https://oracledataviz.blogspot.com/search?q=hyper
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Hi @Ferdy Hermawan if you are interested in training ML models without going throug that tuning manually, you should leverage the Auto ML capability of OAC. Check this video as a brief example for this :
if not, if you need the manual process, in addition to the blogs above, this old video should still show most of the process :
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Hi @Ferdy Hermawan if you are talking about the 'Explain' feature itself, yes, it's included in any OA instance by default (no plugin or extra install). It currently works with Datasets, but we are enhancing it to support Subject Areas next calendar year. The following short videos will give you a brief overview :
let us know if not what you meant
thanks
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Answers
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Hi @Ferdy Hermawan,
Welcome to the community!
You would need to have a basic understanding of the chosen predictive algorithm (i.e. CART, etc.) to be able to understand those values.
Other comments, welcomed.1 -
I would recommend to start with the documentation:
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Many Thanks. finally there is explanation regarding this parameter.
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Hi, thanks for the information.
May i ask for more? in the first Video there is auto ML feature where it can show extra menu that start with "explain <attribute from subject area>", is this feature already embedded in the current OA or i need to upload any plugin to enable the "explain <attribute from subject area>" menu?
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