Methods › General › Deep Tabular Learning › DNN2LR
DNN2LR
Introduced by Zhaocheng Liu et al. in DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DNN2LR is an automatic feature crossing method to find feature interactions in a deep neural network, and use them as cross features in logistic regression. In general, DNN2LR consists of two steps: (1) generating a compact and accurate candidate set of cross feature fields; (2) searching in the candidate set for the final cross feature fields.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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DNN2LR: Automatic Feature Crossing for Credit Scoring 24 Feb 2021 · 0 repositories · arXiv:2102.12036
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DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data 22 Aug 2020 · 0 repositories · arXiv:2008.09775
Tasks archive 2025-07-28
1 task the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Feature Engineering | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections