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 191–200 of 1706 papers

TitleStatusHype
PoliPrompt: A High-Performance Cost-Effective LLM-Based Text Classification Framework for Political Science—0
LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition—0
Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach—0
Android Malware Detection Based on RGB Images and Multi-feature Fusion—0
gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning MethodCode0
Obfuscated Memory Malware Detection—0
Improving Radiography Machine Learning Workflows via Metadata Management for Training Data Selection—0
Graph Classification via Reference Distribution Learning: Theory and Practice—0
Transfer Learning and the Early Estimation of Single-Photon Source Quality using Machine Learning MethodsCode0
Improved Differential Evolution based Feature Selection through Quantum, Chaos, and Lasso—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified