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 1091–1100 of 1706 papers

TitleStatusHype
Stance Detection with Hierarchical Attention Network—0
Statistical and machine learning ensemble modelling to forecast sea surface temperature—0
Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review—0
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading—0
String Theory: Parsed Categoric Encodings with Automunge—0
Structural Representations for Learning Relations between Pairs of Texts—0
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications—0
Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin—0
Supervised Learning based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study—0
Supervised learning on heterogeneous, attributed entities interacting over time—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified