A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)
Hi :wave:
First of all, thanks for this amazing work !
I really like the transparency of the approach brought by the confidence vectors and since they are bounded between [0,1], it is also quite interpretable. In this way, I was wondering if you plan to add a method to "tweek" these values manually ?
For instance, while it cannot be significant in the available training data, an expert could, by analysing the results with the Live test session, identify new words of importance.
Since one can already extract cv values with clf.cv(ngram, cat), do you plan to add a method, for instance set_custom_cv(ngram, cat) ?
Hi :wave: First of all, thanks for this amazing work !
I really like the transparency of the approach brought by the confidence vectors and since they are bounded between [0,1], it is also quite interpretable. In this way, I was wondering if you plan to add a method to "tweek" these values manually ?
For instance, while it cannot be significant in the available training data, an expert could, by analysing the results with the Live test session, identify new words of importance.
Since one can already extract cv values with
clf.cv(ngram, cat), do you plan to add a method, for instanceset_custom_cv(ngram, cat)?Thanks.