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 961–970 of 1706 papers

TitleStatusHype
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media DataCode0
Classical Machine Learning Techniques in the Search of Extrasolar PlanetsCode0
DeepAtom: A Framework for Protein-Ligand Binding Affinity PredictionCode0
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted FeaturesCode0
Projective Quadratic Regression for Online Learning—0
Cross-Class Relevance Learning for Temporal Concept Localization—0
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification—0
Convolutional Neural Network for Convective Storm Nowcasting Using 3D Doppler Weather Radar Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified