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Alteryx Designer Ideas

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I'm really liking the new assisted modelling capabilities released in 2020.2, but it should not error if the data contains: spatial, blob, date, datetime, or datetime types.

 

This is essentially telling the user to add an extra step of adding a select before the assisted modelling tool and then a join after the models. I think the tool should be able to read in and through these field types (especially dates) and just not use them in any of the modelling.

 

An even better enhancement would be to transform date as part of the assisted modelling into something usable for the modelling (season, month, day of week, etc.)

 

joe_lipski_0-1593515364178.png

 

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Can we have a tool to optimize another tool's configuration based on an output target? For example optimize the fuzzy logic setup to find the optimal tool configuration that yields the best matching score for a given data set.

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Hello All,

 

During my trial of assisted modelling, I've enjoyed how well guided the process is, however, I've come across one area for improvement that would help those (including myself) overcome any hurdles when getting started.

 

When I ran my first model, I was presented with an error stating that certain fields had classes in the validation dataset that were not present in the Training dataset.

 

Upon investigation (and the Alteryx Community!) I discovered that this was due to a step in the One Hot Encoding tool.

 

Basically, the Default setting is for all fields to be set to error under the step for dealing with values not present in the training dataset, but there is an option to ignore these scenarios.

 

My suggestion:

Add an additional step to Assisted Modelling that gives the user the option to Ignore / Error as they see fit.

If this were to be implemented then it would remove the only barrier I could find in Assisted Modelling.

 

Hope this is useful and happy to provide further context / details if needed.

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It would be great if it was possible to output the top most influential features in producing the score for each individual entity/row when using the predictive and machine learning tools.

 

Similar to the way they work in DataRobot. Details here and here.

 

This would enable some simple interpretation of how a model came to an individual prediction and the most important features in that particular row/case.

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Model evaluation (including feature importance) is only available in assisted modelling within the machine learning tools.

 

It would be great if there was a tool to do this when using the expert mode so that you could see some standard performance metrics for your model(s) and view the feature importance.

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Assisted modeling is a great idea but right now it's a bit unflexible.

IMHO the greatest strength is the semi-automated transform tool, which would extremely helpful on its own.

 

What would be great is the possibility of using the features without having to go through the assisted modeling wizard but as single tools with minimal configuration, so that it could be used as an automated system for quickly choosing variables.

This way we could have a pretty much perfect rapid prototyping tool for machine learning tasks, leaving more freedom in modeling and enabling less skilled analysts in easily finding on which variables they should focus.

 

What do you think?

  • Category Machine Learning
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Hello,

 

as shown in the Alteryx Inspire Demo, Assisted modeling is going to work with a wizard and generate several tools as result.

The data evaluation functions and feature engineering assist however would be extremely useful tools in their own, is there any chance we can use them as separate tools in the upcoming version?

 

Thanks in advance!

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