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 881–890 of 1706 papers

TitleStatusHype
Binary Classification as a Phase Separation ProcessCode0
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework—0
A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo—0
A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction—0
LAC : LSTM AUTOENCODER with Community for Insider Threat DetectionCode0
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering—0
Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge—0
Machine Learning and Feature Engineering for Predicting Pulse Status during Chest CompressionsCode0
Machine learning for complete intersection Calabi-Yau manifolds: a methodological studyCode0
Low Dimensional State Representation Learning with Reward-shaped Priors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified