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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 1701–1706 of 1706 papers

TitleStatusHype
Supervised and Unsupervised Neural Approaches to Text ReadabilityCode0
Graph Convolutional Networks for Named Entity RecognitionCode0
Graph Convolutional Networks for Named Entity RecognitionCode0
Graph Convolutional Neural Networks for analysis of EEG signals, BCI applicationCode0
Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural NetworksCode0
An Empirical Analysis of Feature Engineering for Predictive ModelingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified