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 741–750 of 1706 papers

TitleStatusHype
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction—0
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems—0
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification—0
Feature Cross Search via Submodular Optimization—0
Feature Engineering and Classification Models for Partial Discharge in Power Transformers—0
Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction—0
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
An Efficient Architecture for Predicting the Case of Characters using Sequence Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified