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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 541–550 of 1706 papers

TitleStatusHype
Cross-lingual Short-text Matching with Deep Learning—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
A machine learning model for identifying cyclic alternating patterns in the sleeping brain—0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
Cross-Class Relevance Learning for Temporal Concept Localization—0
Assets Forecasting with Feature Engineering and Transformation Methods for LightGBM—0
Credit card fraud detection using machine learning: A survey—0
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data—0
Asset price movement prediction using empirical mode decomposition and Gaussian mixture models—0
A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified