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 1041–1050 of 1706 papers

TitleStatusHype
Techniques for Automated Machine Learning—0
Dynamic Malware Analysis with Feature Engineering and Feature LearningCode0
Medical Concept Representation Learning from Claims Data and Application to Health Plan Payment Risk Adjustment—0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
Deep Neural Baselines for Computational Paralinguistics—0
Encoding high-cardinality string categorical variablesCode0
An Enhanced Ad Event-Prediction Method Based on Feature Engineering—0
Danish Stance Classification and Rumour ResolutionCode0
Complex Word Identification as a Sequence Labelling TaskCode0
Multilingual and Multitarget Hate Speech Detection in Tweets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified