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
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
From Features to Transformers: Redefining Ranking for Scalable Impact—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified