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

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Showing 971–980 of 1706 papers

TitleStatusHype
Learning based Methods for Code Runtime Complexity Prediction—0
Semi-Supervised Semantic Role Labeling with Cross-View Training—0
A System for Diacritizing Four Varieties of Arabic—0
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News ClassificationCode0
A Survey on Recent Advances in Named Entity Recognition from Deep Learning modelsCode0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified