Domain Adaptation with Hierarchical Logistic Regression
Bob Carpenter continues his series on domain adaptation:
Last post, I explained how to build hierarchical naive Bayes models for domain adaptation. That post covered the basic problem setup and motivation for hierarchical models.
Hierarchical Logistic Regression
Today, we’ll look at the so-called (in NLP) “discriminative” version of the domain adaptation problem. Specifically, using logistic regression. For simplicity, we’ll stick to the binary case, though this could all be generalized to K-way classifiers.
Logistic regression is more flexible than naive Bayes in allowing other features (aka predictors) to be brought in along with the words themselves. We’ll start with just the words, so the basic setup look more like naive Bayes.