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 1191–1200 of 1706 papers

TitleStatusHype
UMUTextStats: A linguistic feature extraction tool for Spanish—0
Ensemble learning for predictive uncertainty estimation with application to the correction of satellite precipitation products—0
Understanding Generative AI Content with Embedding Models—0
Understanding LLM Embeddings for Regression—0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes—0
Unified Embedding Based Personalized Retrieval in Etsy Search—0
Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition—0
UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis—0
UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling—0
Show:102550
← PrevPage 120 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified