Perform several Trainings into 1 solution and get each of them separately

DJamin 386 Reputation points
2021-06-29T08:29:37.157+00:00

Dear experts,
I am a new user of ML.NET and I am not clear about how to perform my needs.
My environment is the following :
windows 10, .NET Core 3.1 LTS and latest Visual Studio.

What I want is to train a ML to get 5 Labels form 1 Feature. From the tutorial I have seen the example of 1 Feature -> 1 Label training and I am not sure if it is possible to extend the Text classification for my case.

This is a simple example dataset of what I want to achieve :

1/ Feature = The cat lenght is 30cm
-> Animal=The cat / Extra= / Prop=lenght is / quantity=30 / unit=cm

2/ Feature = The dog density is 100g/cm3
-> Animal=The dog / Extra= / Prop=density is / quantity=100 / unit=g/cm3

3/ Feature = The horse number 2 speed is 45km/h
-> Animal=The horse / Extra=number 2 / Prop=speed is / quantity=45 / unit=km/h

1 Feature input -> 5 Labels to determine : Animal, Extra, Prop, quantity, unit.
I have a list of Feature and Labels into my existing dataset, then I want to apply the result of the training on whatever new Feature not in my dataset and find the 5 Labels above.

I am thinking about making 1 training for each Label in cascade.
A/ Train a ML to get Label=unit from Feature.
B/ Train a ML to get Label=quantity from Feature+unit.
C/ ...
and so on until I get the 5 Labels trainings.

I am not clear with 2 points :

  • I have already done the 1st training and created the app into my solution.
    I want to perform my second training into this solution. How can I do that ?
  • In the end I want to get the result of the 5 trainings on an independant Feature.
    How can I access each trainings separately?

Best regards,
David.

.NET Machine learning
.NET Machine learning
.NET: Microsoft Technologies based on the .NET software framework.Machine learning: A type of artificial intelligence focused on enabling computers to use observed data to evolve new behaviors that have not been explicitly programmed.
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