I have a machine learning classifier from create ML. The model is trained with 3400 samples and overall is impressive in how accurate it is. However The model ever so often makes a prediction that is wrong and I can't seem to figure out how I add this into the model so that it does not have such high confidence on some of these wrong prediction. How/can I re-train the model with this new piece of data that is wrong in its classification that it is not of this classification? Should I be making a new classification folder and add these wrong classification to it or is there a way in training the model to pass in as a not classification type so it can try to understand the difference between them?
Disclaimer: So far I have not been working with createML. As I understand from the question you provide your training data via a folder structure and training and evaluation is then done by pressing a button. Correct me if I have made some bad assumption.
It would be nice to know what kind of model / architecture you are using and how your training samples look like.
To me your issue sounds like these poorly predicted samples might be underrepresented in your overall dataset. There are a few tricks you can try here:
Depending on your sample type, there are augmentation packages for Python available, and they are pretty easy and straight forward to use.