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What are the most important attributes that distinguish 3 nominal labels from each other?
Newbie
I have a problem where I do not know which model is suitable:
I have 3 nominal labels (1964, 1984, 1994). For all three labels, structural metrics (attributes) of the landscape (PD, Shape, ...) were calculated.
My question: What are the most important attributes that distinguish all 3 labels from each other?
I have 3 nominal labels (1964, 1984, 1994). For all three labels, structural metrics (attributes) of the landscape (PD, Shape, ...) were calculated.
My question: What are the most important attributes that distinguish all 3 labels from each other?
Which model do I have to use here to be able to answer my question?
Many thanks for your help
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Answers
AutoModel can be used to automatically test some machine learning algorithms on your data and also to get an assessment of attribute importance.
If you don't have that available, you can use some of the "Weight by" operators. There is no "best" among those, so you'll need to try at least some and summarize their results. Just as there are machine learning algorithms with different approaches, determining the importance or weight of attributes depends on the approach taken.
Regards,
Balázs
I will perhaps reduce the set of possible variables to a few. Maybe this is a good first step.
yes, in machine learning the importance of attributes can differ between the algorithms being used, but also between data sets.
You could always build a process that loops over different samples of the data, sets the three label attributes in a loop one by one, and then uses some of the Weight by ... operators to calculate the attribute importance for that sample, that label and that algorithm. Summarizing the results will possibly keep you some insights on the overall importance. You'll probably need "Weights to Data" to convert the weight table to a normal data table.
Regards,
Balázs
I don't have a readily available sample workflow. This is a complex process. But you're trying to solve a complex problem, so that's expected.
The outer loop (for the samples) could be a plain Loop operator.
Inside that I would use Loop Attributes and select the three possible labels as the attributes to loop on.
Inside that loop, you could use another Loop and Select Subprocess with the different learning operators to get the weights.
At the end of most loops you'll receive a Collection of tables. You can use Append to convert these collections of tables to simpler tables.
Regards,
Balázs
Thank you very much and happy holidays.
Kind regards
Lauschi