I have a two ML models model_a and model_b that optimize on an event, label_a.
- I have a small volume of labels for model_a and a large volume of labels for model_b.
- The features used in these models have a large amount of overlap, ~80%.
- model_b is trained with labels that may only be 45-60% accurate in the context of model_a.
I'd like to use model_b label data to train model_a. I'm considering two approaches:
- Use the labels directly for model_a, weighting the labels if their accuracy is known.
- Use transfer learning to apply the learning from trained model_b to model_a before training on the smaller corpus of model_a labels
How should I be thinking about the tradeoffs between these two methods? Is one likely to be more successful than the other? Are there alternative options I've not considered?