[I’m using fastai v1.0.60]
I’m attempting to evaluate/interpret my trained multilabel text classification model. Unfortunately the ClassificationInterprerter doesn’t seem to be set up with multilabels in mind, so struggling a bit to check what my model is doing. So far:
I’ve come across plot_multi_losses - but this only seems to work with image data
ClassificationInterpreter will spit out predictions as a list of class numbers instead of a list one-hot encoded tensors, so seems like I can’t really use interp at the moment.
I have manually extracted the model preds as one-hot encoded tensors (new_preds):
preds,y,losses = learn.get_preds(with_loss=True)
new_preds = (preds>0.7).type(ByteTensor)
Where 0.7 is my threshold and y is the list of one-hot encoded true labels.
Question is, what do I do with this? Does anyone have any experience interpreting multi-label models?
I’m struggling to visualize how a confusion matrix would work/be calculated in this context.