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 211–220 of 1706 papers

TitleStatusHype
PainDECOG: Machine Learning-Based Identification of Pain Biomarkers from sEEG Signals—0
SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image—0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Chronic Diseases Prediction Using ML—0
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
Decision Tree Based Wrappers for Hearing Loss—0
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
Enhancing Physics-Informed Neural Networks Through Feature Engineering—0
Exploring Patterns Behind Sports—0
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified