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 1311–1320 of 1706 papers

TitleStatusHype
Convolutional Neural Network with Word Embeddings for Chinese Word SegmentationCode0
p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning—0
Event Argument Identification on Dependency Graphs with Bidirectional LSTMs—0
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory—0
Deep Learning in Lexical Analysis and Parsing—0
Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure—0
Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble—0
End-to-End Optimized Speech Coding with Deep Neural Networks—0
Deep Health Care Text Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified