SOTAVerified

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 621–630 of 1706 papers

TitleStatusHype
BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using PhotoplethysmogramCode1
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor FactorizationCode1
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles—0
Understanding the Dynamics of DNNs Using Graph ModularityCode1
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge DevicesCode1
Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks—0
A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities—0
Innovative Measures of Patient and Disease Phenotyping: Optimizing Linguistic and Machine Learning Techniques in the Investigation of Electronic Health Record (EHR) Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified