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 991–1000 of 1706 papers

TitleStatusHype
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection TechniqueCode0
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications—0
RiWalk: Fast Structural Node Embedding via Role IdentificationCode0
Geomancer: An Open-Source Framework for Geospatial Feature Engineering—0
Differentiable Sparsification for Deep Neural Networks—0
A Conditional Generative Model for Predicting Material Microstructures from Processing Methods—0
Character Feature Engineering for Japanese Word Segmentation—0
Intrusion detection systems using classical machine learning techniques versus integrated unsupervised feature learning and deep neural network—0
A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting—0
Revisiting the Role of Feature Engineering for Compound Type Identification in Sanskrit—0
Show:102550
← PrevPage 100 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified