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 1291–1300 of 1706 papers

TitleStatusHype
Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering—0
ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact—0
ML-powered KQI estimation for XR services. A case study on 360-Video—0
MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problems—0
Mnemosyne: Learning to Train Transformers with Transformers—0
Mode Effects' Challenge to Authorship Attribution—0
Model-Agnostic Interpretability of Machine Learning—0
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates—0
Modeling Skip-Grams for Event Detection with Convolutional Neural Networks—0
Modeling Story Expectations to Understand Engagement: A Generative Framework Using LLMs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified