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
Machine Learning for Wireless Link Quality Estimation: A Survey—0
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals—0
AnyThreat: An Opportunistic Knowledge Discovery Approach to Insider Threat Detection—0
Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models—0
Lagged correlation-based deep learning for directional trend change prediction in financial time series—0
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data—0
Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data—0
Robust cross-domain disfluency detection with pattern match networks—0
ML-Net: multi-label classification of biomedical texts with deep neural networksCode0
Cross-lingual Short-text Matching with Deep Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified