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 991–1000 of 1706 papers

TitleStatusHype
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained EnvironmentsCode0
Deep learning approach to control of prosthetic hands with electromyography signals—0
Slices of Attention in Asynchronous Video Job Interviews—0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
Statistical and machine learning ensemble modelling to forecast sea surface temperature—0
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing—0
A Robust Hybrid Approach for Textual Document ClassificationCode0
Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable EnergyCode0
Deep Learning Regression of VLSI Plasma Etch Metrology—0
Multi-Task Bidirectional Transformer Representations for Irony Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified