Deep Tabular Learning
DNN2LR
Introduced by Liu et al. in DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular DataDNN2LR 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.
Source: DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
