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 821–830 of 1706 papers

TitleStatusHype
Geometric feature performance under downsampling for EEG classification tasks—0
Getting the Most out of AMR Parsing—0
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems—0
Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation—0
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition—0
Global Pose Estimation with an Attention-based Recurrent Network—0
Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network—0
GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text—0
GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees—0
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified