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 961–970 of 1706 papers

TitleStatusHype
Predictive Precompute with Recurrent Neural Networks—0
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network—0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
Print Defect Mapping with Semantic Segmentation—0
Product age based demand forecast model for fashion retail—0
Projective Quadratic Regression for Online Learning—0
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey—0
Pseudo-Labels Are All You Need—0
QoSBERT: An Uncertainty-Aware Approach based on Pre-trained Language Models for Service Quality Prediction—0
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified