SOTAVerified

Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 1541–1550 of 1706 papers

TitleStatusHype
Recurrent neural network models for disease name recognition using domain invariant features—0
Relation extraction from clinical texts using domain invariant convolutional neural network—0
Learning Concept Taxonomies from Multi-modal Data—0
Incremental Parsing with Minimal Features Using Bi-Directional LSTM—0
Model-Agnostic Interpretability of Machine Learning—0
Neural Word Segmentation Learning for ChineseCode0
De-identification of Patient Notes with Recurrent Neural NetworksCode0
Learning Stylometric Representations for Authorship Analysis—0
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning—0
Dynamic Feature Induction: The Last Gist to the State-of-the-Art—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified