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Bumped by Community user
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Alexey S. Larionov
Alexey S. Larionov
  • 113
  • 5

Very new in ML...

Actual problem is more complex, but I'll give it shorter.

Train data is a collection of samples, describing features of some items. There are many samples for each item. So ML model is implied to predict a few target variables, but one time for each item:

[![enter image description here][1]][1] [1]: https://i.sstatic.net/P1fWT.png

It appears, that I can't find in sklearn or a similar kit a way to fit model with this type of train-input/train-output. Is there any way to do this without hand-implementing whole pipeline?

I've considered this answer: How to predict based on multiple samples?

But I'm not sure it's applicable, since there are many-many samples for each item

Edit 1: Final model should get a few new samples for completely new item and predict same target variables

Very new in ML...

Actual problem is more complex, but I'll give it shorter.

Train data is a collection of samples, describing features of some items. There are many samples for each item. So ML model is implied to predict a few target variables, but one time for each item:

[![enter image description here][1]][1] [1]: https://i.sstatic.net/P1fWT.png

It appears, that I can't find in sklearn or a similar kit a way to fit model with this type of train-input/train-output. Is there any way to do this without hand-implementing whole pipeline?

I've considered this answer: How to predict based on multiple samples?

But I'm not sure it's applicable, since there are many-many samples for each item

Very new in ML...

Actual problem is more complex, but I'll give it shorter.

Train data is a collection of samples, describing features of some items. There are many samples for each item. So ML model is implied to predict a few target variables, but one time for each item:

[![enter image description here][1]][1] [1]: https://i.sstatic.net/P1fWT.png

It appears, that I can't find in sklearn or a similar kit a way to fit model with this type of train-input/train-output. Is there any way to do this without hand-implementing whole pipeline?

I've considered this answer: How to predict based on multiple samples?

But I'm not sure it's applicable, since there are many-many samples for each item

Edit 1: Final model should get a few new samples for completely new item and predict same target variables

Source Link
Alexey S. Larionov
Alexey S. Larionov
  • 113
  • 5

How to train model to predict 1 value from multiple input samples

Very new in ML...

Actual problem is more complex, but I'll give it shorter.

Train data is a collection of samples, describing features of some items. There are many samples for each item. So ML model is implied to predict a few target variables, but one time for each item:

[![enter image description here][1]][1] [1]: https://i.sstatic.net/P1fWT.png

It appears, that I can't find in sklearn or a similar kit a way to fit model with this type of train-input/train-output. Is there any way to do this without hand-implementing whole pipeline?

I've considered this answer: How to predict based on multiple samples?

But I'm not sure it's applicable, since there are many-many samples for each item