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 511–520 of 1706 papers

TitleStatusHype
AutoML for Contextual Bandits—0
A Neural Network Based Explainable Recommender System—0
Automation of Feature Engineering for IoT Analytics—0
Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques—0
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval—0
Automating Feature Engineering—0
Automatic Seizure Prediction using CNN and LSTM—0
AEFE: Automatic Embedded Feature Engineering for Categorical Features—0
3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review—0
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified