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 281–290 of 1706 papers

TitleStatusHype
Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces—0
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image ManipulationCode2
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
Ensemble learning for predictive uncertainty estimation with application to the correction of satellite precipitation products—0
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model—0
Defect Detection in Tire X-Ray Images: Conventional Methods Meet Deep Structures—0
Machine Learning-Based Completions Sequencing for Well Performance OptimizationCode0
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
AdvanceSplice: Integrating N-gram one-hot encoding and ensemble modeling for enhanced accuracyCode0
Show:102550
← PrevPage 29 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified