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 981–990 of 1706 papers

TitleStatusHype
RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition—0
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems—0
Real-Time Well Log Prediction From Drilling Data Using Deep Learning—0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Recognizing irregular entities in biomedical text via deep neural networks—0
Recognizing Salient Entities in Shopping Queries—0
ReConTab: Regularized Contrastive Representation Learning for Tabular Data—0
Recurrent Convolutional Neural Networks for Discourse Compositionality—0
Recurrent neural network models for disease name recognition using domain invariant features—0
Recurrent Neural Networks for Time Series Forecasting—0
Show:102550
← PrevPage 99 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified