Improving ML.NET accuracy by connecting 2 neural networks #AI #ML
In this case, I am using the Issue Classification scenario in ML.NET, where I initially trained a network to predict an output on 24 inputs. The result was a very poor 10%, but I had an idea, to create a second training set, based on the inital inputs, plus the outputs (The SCORE array) of the network (31 elements).
By creating a new table, with the initial 24 inputs, the training output, and the scrore array, i.e. another 31 floats, I then created a table that was 56 columns wide, and I used that again to train on ML.NET.
Using this second-level neural network, I effectively created a neural network with twice the number of layers, and the accuracy went from 10% to 30%.
I am hoping to repeat this process another number of times to gradually increase the accuracy up to a usable level of 90%.
No code examples yet; but if the repeated process actually increases this right up to 90%+, then I’ll share!
