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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 681–690 of 1706 papers

TitleStatusHype
Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction—0
Estimating Brain Age with Global and Local Dependencies—0
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks—0
Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation—0
Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector—0
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
An Unsupervised Model with Attention Autoencoders for Question Retrieval—0
Event Argument Identification on Dependency Graphs with Bidirectional LSTMs—0
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified