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 1321–1330 of 1706 papers

TitleStatusHype
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling—0
Multi-Task Bidirectional Transformer Representations for Irony Detection—0
Multi-Task Cross-Lingual Sequence Tagging from Scratch—0
Multitask Learning with Deep Neural Networks for Community Question Answering—0
Multi-View Feature Engineering and Learning—0
Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping—0
NCSU-SAS-Ning: Candidate Generation and Feature Engineering for Supervised Lexical Normalization—0
Neighborhood Adaptive Estimators for Causal Inference under Network Interference—0
NER for Medical Entities in Twitter using Sequence to Sequence Neural Networks—0
Show:102550
← PrevPage 133 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified