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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–60 of 1706 papers

TitleStatusHype
Feature Engineering on LMS Data to Optimize Student Performance Prediction—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science—0
FeRG-LLM : Feature Engineering by Reason Generation Large Language Models—0
RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts—0
Embedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline—0
Feature-Enhanced Machine Learning for All-Cause Mortality Prediction in Healthcare Data—0
Asset price movement prediction using empirical mode decomposition and Gaussian mixture models—0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine LearningCode3
Show:102550
← PrevPage 6 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified