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 1–10 of 1706 papers

TitleStatusHype
Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction—0
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey—0
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—0
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners—0
Tabular Feature Discovery With Reasoning Type Exploration—0
Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures—0
Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation—0
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
Feature Engineering for Agents: An Adaptive Cognitive Architecture for Interpretable ML Monitoring—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified