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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 1371–1380 of 1706 papers

TitleStatusHype
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
On the effectiveness of feature set augmentation using clusters of word embeddings—0
On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning—0
On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification—0
On the Replicability and Reproducibility of Deep Learning in Software Engineering—0
Deep Learning Chromatic and Clique Numbers of GraphsCode0
Deep Learning for Answer Sentence SelectionCode0
Classical Machine Learning Techniques in the Search of Extrasolar PlanetsCode0
Machine learning for complete intersection Calabi-Yau manifolds: a methodological studyCode0
What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtractionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified