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For encoding text/high-cardinality categories, ATM we have MinHashEncoder, which only works when the downstream learner is based on decision trees, and GapEncoder, which gives high-quality representations but is very slow. It would be good to have something similar to the GapEncoder but faster, maybe a SVD or scikit-learn's NMF
Feature Description
an encoder that works similarly to GapEncoder but is faster, possibly at the cost of less interpretable topics or slightly reduced prediction performance
The text was updated successfully, but these errors were encountered:
Problem Description
For encoding text/high-cardinality categories, ATM we have MinHashEncoder, which only works when the downstream learner is based on decision trees, and GapEncoder, which gives high-quality representations but is very slow. It would be good to have something similar to the GapEncoder but faster, maybe a SVD or scikit-learn's NMF
Feature Description
an encoder that works similarly to GapEncoder but is faster, possibly at the cost of less interpretable topics or slightly reduced prediction performance
The text was updated successfully, but these errors were encountered: