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 1111–1120 of 1706 papers

TitleStatusHype
Hybridization of Persistent Homology with Neural Networks for Time-Series Prediction: A Case Study in Wave Height—0
Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization—0
ICE: Information Credibility Evaluation on Social Media via Representation Learning—0
ICL’s Submission to the WMT21 Critical Error Detection Shared Task—0
ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge—0
Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks—0
Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory—0
Identifying Real Estate Opportunities using Machine Learning—0
Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach—0
Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified