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[SOLVED] Multiple Output

DoninhasDoninhas Member Posts: 3 Contributor I
edited November 2018 in Help
Hey there,

my task is to predict frequency spectra from two different measurements. I measure with 3 sensors, so each measurement has 3 entries as columns.
A was measured under an different environment than B.

The data have the following shape:
Measurement A:
Measurement B:
The thesis is that I can use a stochastic approach to predict B using A. So I make a measurement C under the same conditions as A and want to use RM to predict D (which will have the same environment as B).

I hope you understand what I want to try.

The question is now how I can realize this with RM. I already thought Multi Label is the answer, but apparently I misunderstood what it does.
Is it even possible to create D with its three column? Do you have any advice?

Thanks in advance!


  • frasfras Member Posts: 93 Contributor II
    Sounds interesting.But your task is really hard to understand. It would be helpfull if you
    could describe your problem with words like trainset, testset, label attribute, polynominal, binominal,...
    Moreover, an example process is always helpful. Is your task really a supervised learning problem or
    more unsupervised (clustering) ?
  • DoninhasDoninhas Member Posts: 3 Contributor I

    thanks for your answer. I found a solution myself, using loop label  :)

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