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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 1101–1110 of 1706 papers

TitleStatusHype
Supervised Typing of Big Graphs using Semantic Embeddings—0
Survey on Embedding Models for Knowledge Graph and its Applications—0
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues—0
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection—0
Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks—0
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields—0
Syntax Aware LSTM Model for Chinese Semantic Role Labeling—0
Syntax Aware LSTM model for Semantic Role Labeling—0
Syntax Encoding with Application in Authorship Attribution—0
Systematic Literature Review on Application of Machine Learning in Continuous Integration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified