Hi, I'm new to modAL and machine learning. This might be a dumb question but I didn't figure it out after I read through docs and reproduced some of the tutorials.
I have a binary text classification model, imdb reviews (positive or negative). I want to know the uncertainty score of each instances in the pool after query.
I'm using a for loop to loop through all elements in the tfidf_transformed matrix, and get classifier_uncertainty(learner, element).
r = [classifier_uncertainty(self.learner, i) for i in self.x_pool_vec]
there are more than 6000 elements in the matrix and each round of the iteration takes proximately 0.13..... secs.
I wonder is there any more efficient way I get get the things I need?
Hi, I'm new to modAL and machine learning. This might be a dumb question but I didn't figure it out after I read through docs and reproduced some of the tutorials.
I have a binary text classification model, imdb reviews (positive or negative). I want to know the uncertainty score of each instances in the pool after query.
I'm using a for loop to loop through all elements in the tfidf_transformed matrix, and get classifier_uncertainty(learner, element).
r = [classifier_uncertainty(self.learner, i) for i in self.x_pool_vec]there are more than 6000 elements in the matrix and each round of the iteration takes proximately 0.13..... secs.
I wonder is there any more efficient way I get get the things I need?
Thank you so much!
I can provide more code if needed.