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 1231–1240 of 1706 papers

TitleStatusHype
Learning to Focus when Ranking Answers—0
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models—0
Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation—0
LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems—0
Leveraging Affective Bidirectional Transformers for Offensive Language Detection—0
Leveraging Contextual Information for Effective Entity Salience Detection—0
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading—0
Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases—0
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time—0
Leveraging Latent Representations of Speech for Indian Language Identification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified