Logistic regression and other parametric, non-regularized linear learners tend to do poorly with NLP forecasting -type modeling. (They usually overfit.)
That's why you build another threshold-type learner and then apply logistic regression to convert the score produced by learner A into a probability score.
Then you can tune at the exact point of the precision-recall curve that maximizes business value.
That's why you build another threshold-type learner and then apply logistic regression to convert the score produced by learner A into a probability score.
Then you can tune at the exact point of the precision-recall curve that maximizes business value.