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 781–790 of 1706 papers

TitleStatusHype
FenceNet: Fine-grained Footwork Recognition in Fencing—0
FeRG-LLM : Feature Engineering by Reason Generation Large Language Models—0
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language—0
Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques—0
Few-shot incremental learning in the context of solar cell quality inspection—0
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection—0
Field-aware Neural Factorization Machine for Click-Through Rate Prediction—0
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning—0
Event Argument Identification on Dependency Graphs with Bidirectional LSTMs—0
Bridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified