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 631–640 of 1706 papers

TitleStatusHype
Vibration fault detection in wind turbines based on normal behaviour models without feature engineering—0
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach—0
Human heuristics for AI-generated language are flawed—0
Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning—0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
UMUTextStats: A linguistic feature extraction tool for Spanish—0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
Towards Context-Aware Neural Performance-Score Synchronisation—0
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction—0
Interpretable Feature Engineering for Time Series Predictors using Attention Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified