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 1161–1170 of 1706 papers

TitleStatusHype
Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or FemaleCode0
Multi-Level Network Embedding with Boosted Low-Rank Matrix ApproximationCode0
MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction—0
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction—0
Learning to Focus when Ranking Answers—0
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection—0
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models—0
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty DetectionCode0
deepQuest: A Framework for Neural-based Quality EstimationCode0
Seq2seq Dependency ParsingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified