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 51–60 of 1706 papers

TitleStatusHype
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability DetectionCode1
BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using PhotoplethysmogramCode1
DoE2Vec: Deep-learning Based Features for Exploratory Landscape AnalysisCode1
Benchmarks and Custom Package for Energy ForecastingCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using WearablesCode1
End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge DevicesCode1
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch LiteratureCode1
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified