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 1211–1220 of 1706 papers

TitleStatusHype
Learning Concept Taxonomies from Multi-modal Data—0
Learning Connective-based Word Representations for Implicit Discourse Relation Identification—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
Learning Contextually Informed Representations for Linear-Time Discourse Parsing—0
Learning Feature Engineering for Classification—0
Learning Feature Representations for Keyphrase Extraction—0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical Notes—0
Learning latent representations for operational nitrogen response rate prediction—0
Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified